Introduction to Artificial Intelligence (AI) Course

Learn basics of modern AI & understand core fundamentals of Artificial Intelligence with this course

  • 50 hours of Instructor led Training
  • Comprehensive Hands-on with Python
  • Covers supervised & unsupervised algorithms
  • Learn Deep Learning Techniques using TensorFlow and Keras
  • Learn to build a computer vision application

Description

Artificial Intelligence has been predicted to be the most in-demand job in the coming years. According to IDC, the total spending on products and services that incorporate Augmented Reality and/or Virtual Reality concepts will soar from 11.4 billion as of 2017, to almost 215 billion by the year 2021. This is great news for AI career aspirants as the demand for such IT professionals will reach the sky in the coming years.

KnowledgeHut’s course will help you learn the basics of modern AI as well as some of the representative applications of AI. You will get into the core fundamentals of AI and learn about programming concepts, including heuristic search and genetic programming, developing games and building intelligent applications that will be used to deliver solutions to problems in organizations and business. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

What You Will Learn

Prerequisites
  • Sound knowledge in Python Programming
  • Familiarity with Data Science

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

  • Python developers who want to build real-world AI applications
  • Python beginners who want a comprehensive learning plan
  • Experienced programmers looking to use AI in their existing technology stacks

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

Learn how to build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you. Develop expertise in popular AI & ML technologies and problem-solving methodologies. Also develop the ability to independently solve business problems using Artificial Intelligence & Machine Learning.

Topics Covered:

  • What is AI?
  • Python for AI
  • Probability & Statistics
  • Visualization Techniques
  • Case Study

Hands-on:

  • Write Python code to analyze, manipulate and visualize data
  • Learn to implement statistical techniques with Microsoft Excel
  • Write Python code using Python library - matplotlib, seaborn to visualize data and represent it graphically
  • Conduct exploratory data analysis in python to identify potential revenue maximisation opportunities and also visualize data

Learning Objectives:

Learn about supervised learning techniques - regression and classification. Also understand various techniques to build Decision Trees.

Topics Covered:

  • Regression (Linear, Multiple and Logistic)
  • Classification (K-NN, Naive Bayes) Techniques
  • Decision Trees
  • Case Study

Hands-on:

This dataset classifies people described by a set of attributes as good or bad credit risks. Using classification techniques, build a model to predict good or bad customers to help the bank decide on granting loans to its customers

Learning Objectives:

Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Also understand the Elbow method and Silhouette method.

Topics Covered:

  • K-means Clustering
  • Hierarchical Clustering
  • High-dimensional Clustering
  • Case Study

Hands-on:

In marketing, if you’re trying to talk to everybody, you’re not reaching anybody.. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.

Learning Objectives:

Learn about bootstrap sampling and its advantages followed by bagging. Boost model performance with Boosting. Through a real-life case study, learn Random Forest and how it helps avoid overfitting compared to decision trees.

Topics Covered:

  • Boosting
  • Bagging
  • Random Forest
  • Case Study

Hands-on:

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform better than a single model.

Learning Objectives:

Understand the basics of RL and its applications in AI. Get an understanding of Markov Decision Processes: Model processes as Markov chains, and learn algorithms for solving optimisation problems. Write Q-learning algorithms to solve complex RL problems.

Topics Covered:

  • Value based methods 
  • Q-learning
  • Policy-based methods

Hands-on: No hands-on

Learning Objectives:

Learn advanced machine learning techniques using the Neural Networks algorithms. Neural Networks can enable pattern recognition based on a large amount of inputs. Learn how NN algorithms work, and end up with an introduction to deep learning.

This module covers various activation functions like sigmoid, hyperbolic-tangent, Rectified Linear Units, Leaky Rectified Linear Units.

Topics Covered:

  • Neural Network Basics
  • Deep Neural Networks
  • TensorFlow using Neural Networks & Deep Learning
  • Case Study

Hands-on:

A research study was aimed at the case of customers’ default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients.

Learning Objectives:

Get started with the Natural language toolkit, and learn the basics of text processing in Python. Learn how to extract features from unstructured text and build machine learning models on text data. Conduct sentiment analysis, learn to parse English sentences and extract meaning from them. Explore the applications of text analytics in new areas and various business domains.

Topics Covered:

  • Statistical NLP and text similarity
  • Text Summarization
  • Syntax and Parsing techniques
  • Semantics and Generation
  • Case Study

Hands-on:

Stock market prediction has been an interesting research topic for many years. Finding an efficient and effective means of studying the market perceptions found its way in different social networking platforms such as Twitter. With proper tools and the help of technology, meaningful and precious information can be gathered, analyzed, and utilized in different areas like in the movement and performance of the stock market.

Learning Objectives:

Learn to use the power of computer vision and play with what you see, detect faces, eyes and other attributes using Haar cascades.

Topics Covered:

  • Convolutional Neural Networks
  • Keras Library for Deep Learning in Python
  • Pre-processing Image Data
  • Object and face recognition using OpenCV
  • Case Study

Hands-on:

While we drive on a highway, we tend to feel sleepy. In this project, using OpenCV and implementing object detection and feature extraction we detect fatigue in real-time and report an alarm which will not only keep a driver attentive while driving but also reduce the number of accidents.

Learning Objectives:

Learn the AI search technique that employs heuristic for its moves. Understand the fundamental concepts of genetic algorithms and visualize the evolution.

Topics Covered:

  • Uniform and heuristic-based search techniques
  • Planning and constraint satisfaction techniques
  • Adversarial search and its uses
  • Case Study

Hands-on:

Use cutting edge AI techniques to teach a computer to play a computer game.

Projects

Classify good and bad customer for bank to decide on granting loans

This dataset classifies people described by a set of attributes as good or bad credit risks. Using classification techniques, build a model to predict good or bad customers to help the bank decide on granting

Read More

Using NLP, find an efficient and effective means of studying the market perceptions found its way in different social networking platforms such as Twitter

Stock market prediction has been an interesting research topic for many years. Finding an efficient and effective means of studying the market perceptions is important in different social networking platforms such as Twitter. With proper tools and the help of technology, meaningful and precious information can be gathered, analyzed, and utilized in different areas like in the movement and performance of the stock market.

Read More

Using Object Detection technique and feature extraction, detect Fatigue Detection

While we drive on a highway, we tend to feel sleepy. In this project, using OpenCV and implementing object detection and feature extraction we detect fatigue in real-time and report an alarm which will not only keep a driver attentive while driving but also reduce number of accidents.

Read More
Note:These were the projects undertaken by students from previous batches.  

Certification Process

Artificial Intelligence Certification Process

For becoming an AI professional, you need to start from the basics. The first step is getting a bachelor's degree in one of the following streams: 

  • Computer Science 
  • Information Technology 
  • Mathematics 
  • Statistics 
  • Economics 
  • Finance 

The next step is improving your technical skills. This includes not only your programming skills but also your knowledge of practices and techniques of software development. To achieve this, you must have theoretical as well as practical knowledge of the following topics: 

  • Software Development Life Cycle
  • Design Patterns
  • Electronics, Instrumentation, and Robotics
  • Deep Learning
  • Neural Networks
  • Machine Learning
  • Statistics
  • Mathematics
  • Modularity, Classes, and OOPs

Business skills also play a key role in the life of an AI professional. You need to have skills like creative thinking, effective communication, analytical problem-solving, and industry knowledge.


To work on all the above-mentioned skills, you have two options. The first one is to go for a master's degree. The second one is to start practicing on your own and continue until you become an expert. AI is an emerging area. If you are opting for a master's degree, you can select Computer Science, Data Science, or Machine Learning. There is a lot of research and discoveries that are going on in these fields. You can use them to work on your thesis. There is also a third option where you earn Industry certifications that will not only add value to your CV but also help you get a thorough understanding of the subject.


Now, let’s discuss in detail the technical and business skills you must acquire in order to be an AI professional: 


1.Technical Skills

  • You must be skilled in programming with an in-depth knowledge of data structures and classes. The most popular programming languages used in the field of AI are Java, R, Python, C++, etc. You need to be familiar with algorithms, linking, and memory management for leveraging hardware to improve speed. 
  • Familiarity with vectors, matrices, and matrix multiplication is also required. Your concepts of derivatives and integrals must be clear. Statistics is an important part of AI. So, it is essential that you have a deep knowledge of its concepts like Mean, Standard Deviations, Gaussian Distributions, etc. Also, to understand models like Gaussian Mixture Models, a Naive Bayes model, and Hidden Markov model, you must have an understanding of probability. 
  • It is very important that you are familiar with algorithm theory and how the algorithm works. Get an understanding of subjects like Gradient Descent, Lagrange, Partial Differential Equation, Convex Optimization, Summations, and Quadratic Programming. 
  • Natural Language Processing is another important skill required in the field of AI. It involves Computer Science and Linguistics. For this, you must be familiar with libraries like NLTK, and Gensim and techniques like Summarization, Sentimental Analysis, and word2vec.
  • Neural Network Architectures are used to deal with problems like Speech Recognition, Image Classification, and Translation.

2.Business Skills

  • Communication skills are required for explaining concepts of AI to people with no experience in the field. Also, since you will be dealing with robotics and electrical professionals, proper communication will make it easier.
  • It is a part of the job of an AI professional to look at data and check out trends. Creativity and Critical Thinking will help you devise new approaches to AI.
  • It is important that you have knowledge of the industry you are working for. This will help you in analyzing the data and creating strategies.

For building a career in AI, you must have specialization in one or more domains of Machine Learning: 

  • Neural Networks - When you are teaching computers how to think through the classification of information, you will need neural networks. With this, the software will be recognizing images, making decisions and predictions with high accuracy.
  • Natural Language Processing - This allows the machine to understand the human language. This helps the machine to make sense of the human languages in a valuable manner. NLP will completely change the way we interface with machines.
  • Deep Learning - It focuses on the tools of machine learning and how you can deploy them for solving problems and making decisions. Deep learning allows the processing of data through neural networks. You can apply it to text, image, and speech for drawing conclusions.

Thanks to its applications across different domains, there are many companies that are using AI and are hiring AI professionals. An example of the use of AI is self-driving cars. AI, in this case, is used for predictive maintenance, scheduling, navigating routes. AI applications have also found to be useful in reducing crime, improving safety and saving energy. Big brands like Amazon, IBM, Accenture, and Microsoft, are using AI for driving innovation. More and more industries are using AI to drive growth in the market.


If you want a career in AI, you need to follow a learning path. The type of learning path you choose depends on your profession; whether you are a beginner, a programmer, or working in the field of data science. Also, depending on the industry you are working in, you need to acquire different skills. If you are new to the field, you should begin with mathematics and statistics and then, move on to the machine learning courses. Since you are going to be working in the field of AI, you must have programming skills and an understanding of computers and algorithms. If you are a programmer, you can directly study algorithms and start coding. If you are a data analyst and want to get into AI, you need to have programming skills. Apart from that, you need to learn preparing data, building models, and visualization. According to your specialization, here are the jobs in AI you can go for:

  • Data Mining and Analysis
  • Data Scientist
  • Machine Learning Researcher
  • Machine Learning Engineer
  • AI Engineer
  • Business Intelligence Developer

The pace at which the field of AI is developing is making it difficult to forecast its future. New innovations can take this field into a place we can’t even imagine. Even today, we don’t have enough trained machine learning and AI professionals. This gap is only going to increase in the future. If you want a secure future, AI is the field for you. You need to get certified as soon as possible because the sooner you are trained, the sooner you will get to be a part of this ever-changing field.

If you have chosen to work in the field of AI, you need to get trained. Here are some steps that you should take in order to become an expert in AI:

 

1. Groundwork

For a successful career in AI, you need to have mathematical and coding skills. You must enjoy playing with maths and optimizing algorithms. If you think you are passionate enough, you can start building your career in AI. 

  • Basics

There are a lot of topics you need to cover before you start treading into the field of AI. This includes statistics, probability and linear algebra. Math is the most basic and important thing that you have to cover. Start with matrices, vectors, their transformations and then move on to statistics, dimensionality and statistical tests like chi-square tests, z-test, etc. After this, you need to make yourself familiar with the probability concepts like Bayes theorem. For understanding all these concepts, you need to have a strong foundation of Math.  

  • Programming language

Now that you have mastered the concepts of Math, you need to start working on your programming skills. Now, you need to select a language that can be used in the field of AI. For example, R, Python, and Java are some of the most popular programming languages used in the field of AI. Python comes with a lot of libraries that will benefit you in the future. You should go with JAVA only if you have a good background in Computer Science. 

  • Data Structures

Data structures will help in designing systems that will be used to solve problems of data. This system must be optimized and accurate.  

  • Regression

Regression is the implementation of math. It will help in making predictions in real-life applications. 

  • Machine learning models

You can start playing with machine learning algorithms like KNN, Decision Trees, Random Forests, SVM, etc. You need to implement them for a better understanding. Make sure that you are understanding the math behind the algorithm. This will help you in customizing them for your own benefit. Next, study the different cases of machine learning algorithms. 


2. Moving into AI 

Now that you have covered the basics, you can move onto deep learning. This involves understanding neural networks, the math behind them, their types, understanding AI in Intelligent Systems and NLP, and getting acquainted with the concepts of Big Data. 


3. Mastering AI

This is the final step. You need to master the optimization techniques used in the field of AI. Take part in competitions, try reading and publishing a research paper. It will help you get a job in the field. You can also play with algorithms by tweaking the math behind it.

If you are new to the field of AI, don’t worry. You can still get a job. Now, no company is going to hire a professional with zero experience. But work experience is not the only thing they are looking for. Here are some ways you can make up for the lack of experience:


Personal Projects

Your GitHub profile should have at least 2 ML projects. When employers are looking for new recruits, they often eliminate people with no projects. Now, if you have just started working in the field of AI, it will be hard coming up with a project. Don’t worry about this. This project does not need to be big or innovative. It is just for showing the recruiters that you have an understanding of the field, have good coding standards and are able to work independently. When you are building your project, you need to focus on a few things like it shouldn’t take more than a month to finish the project, the code is clean and commented, there are all the relevant documentation, etc.


Hackathon

There are several reasons why hackathons can be a life saver for people with no experience. Not only will you be forced to go out and create something, but you will also get an opportunity to meet and interact with experienced people in the field and make an impressive addition to your CV. There are several AI-specific hackathons. You can also try some general software hackathons and put an AI spin to the project. Visit the website meetup.com to check if there are any AI based meetup groups near you. Such groups usually organize a hackathon every year. 


Coding challenges

These are similar to hackathons where you will be building a practical application. This will help you stay ahead of all the other candidates when you apply for a job in the field of AI. Through these competitions, you will get an opportunity to meet fellow IT professionals from across the country. You will get a sense of competition that will help you stay focused and motivated. Most of these competitions also offer hefty prize money. You can search for such competitions on websites like Kaggle, Halite.io, and CodinGame.


Open source projects

For getting an insight into the real-world projects, open source projects are the way to go. You will get a look at the industry standard code and learn skills like versioning control, debugging, ML, and how to work with other people.

Apart from these, you need to make sure that you have a thorough understanding of all the concepts of Machine Learning and Artificial Intelligence. There are several resources like Stanford Machine Learning, DeepLearning.ai, Grokking Deep Learning, Siraj Raval’s YouTube channel, etc. Also, you must specialize in a particular domain of AI. This will help you become more valuable to the recruiters. Some of these domains include Computer Vision, Recurrent Networks, Reinforcement Learning, Natural Language Processing, Generative Adversarial Networks, Meta Learning, One Shot Learning, Debugging, Neural Network Visualization, etc. Lastly, if anything doesn’t work, go to college. Get a Master’s degree. It will put you in a better position.

As an AI Engineer, here are the roles you can work as: 

  • Research Scientists

They design, undertake and analyze information. 

  • Software Engineer

Work in the areas of development like databases, networks, applications, or operating systems 

  • Information Security Engineers

Help in safeguarding the computer systems and network of the organization 

  • C# Developer

Responsible for handling different aspects that you face while developing an application including performance, testing, security, scalability, etc. 

  • Java Developer

The specialized programmer collaborating with software engineers and web developers for integrating Java into business software, applications, and websites 

  • Software Analyst

Prepares the requirements of the software, studies the domain of the software application, and creates specification documents. 

  • Software Development Manager

Responsible for designing, installing, testing, and maintaining software systems.

Certifications have become an integral part of the life of an IT professional. They help you stand apart from the massive crowd of IT job seekers. They are worth a lot. But there are so many options available that it can be difficult to select the one that is worth spending money on. Before you choose a certification for you, let’s discuss the type of AI certifications available. 

There are two types of AI certification. The first ones are the theoretical certification where you will be learning building AI applications. The other ones are the certifications that help you understand the strategic applications of AI. Here are some of the examples of both types of certifications:

1. Theoretical Certifications 

  • Stanford University, Coursera - This is a highly regarded and popular11 week program on AI. Taught by an ex-Google Brain and the Founder of Coursera, Andrew Nf, it covers the core principles of AI. You will also be made familiar with the programming framework, MATLAB. It is widely recognized by IT companies. The course is available for free. To get the certification, you will have to pay a fee of £58.  
  • Udacity Machine Learning Nano-Degree - This is a career-focused course for professionals who want to start working in the field of Machine Learning. It will take you about 6 months to finish the course. At the end of it, you will not only have all the required knowledge but also have several projects covering different business use cases that you can share with recruiters. The price of this course is 999 Euros.

2. Strategic Applications of AI

  • CFTE’s AI in Finance: This first-of-its-kind course is for IT professionals working in the field of financial services. With 23 experts from the field, you will have a deep understanding of the application and impact of AI in finance. The course starts with an introduction to AI technologies and then move on to its applications in verticals like insurance and asset management. It also provides you with tools for making informed decisions related to AI. It is developed by Ngee Ann Polytechnic Institute, Singapore. The price of the course is £499.

  • MIT- Artificial Intelligence: MIT along withGetSmarter, the e-learning platform, provides this course for helping the students learn the impact of AI on business. With the growing demand for AI and its implications on the business strategy, there is more demand for business professionals who have an understanding of AI. This certification will introduce you to the subject and then provide a capstone project for understanding its application. The cost of this course is £2,500.

The realization that AI can disrupt all industries has led to an increase in the demand for AI experts. There are plenty of jobs and not enough tech talent with the required skills to fill these positions. For a career in the field of AI, there are certain educational requirements. First thing is to get a bachelor’s degree in basic computer science and mathematics. This will make you eligible for entry-level positions. After this, you need to get a Master’s degree for becoming an AI Specialist. In this course, there will be several concepts covered like Bayesian networking, computer science, engineering, robotics, cognitive science theory, physics, and different levels of math including algebra, calculus, probability, statistics, logic, and algorithms. If you are a software engineer, you can try AI-focused courses to become an AI developer.

Before you choose the certification, you must have knowledge of the different roles in the field of AI and their requirements:

  • Machine Learning Engineer - They are some of the most highly paid professionals in the IT industry. They build and manage Machine Learning projects. To work as a Machine Learning engineer, you must have a background in data science and applied research. You must also be an AI programmer and have a thorough knowledge of different programming languages. Their work involves applying predictive models and leveraging NLP with large datasets. You must have experience working with agile development practices and IDE tools like IntelliJ and Eclipse.
  • Data Scientist - The job of a data scientist is to collect, analyze, and interpret big, complicated datasets through predictive analytics and machine learning. They also play an important role in the algorithm’s development that collects and cleans data for analyzing. For a job as a data scientist, you must be comfortable using tools and platforms like Hive, Pig, Hadoop, Spark, and MapReduce. Also, you must be familiar with statistical computing languages and programming languages like Python, SQL, Perl, Scala, etc. You must have at least 2 years of experience working in the field of Machine Learning.
  • Business Intelligence Developer - The role of a Business Intelligence Developer is analyzing complex datasets for identifying market and business trends. They design, model, and maintain complex data in cloud-based platforms. For this job, you need to have analytical and technical skills. You must also be familiar with data mining, data warehouse design, BI technologies, SQL queries, SQL Server Reporting Services, SQL Server Integration Services, etc.
  • Research Scientist - These are the experts in different domains of AI like applied mathematics, deep learning, machine learning, and computational statistics. You must be familiar with graphical models, computer perception, reinforcement learning, natural language processing, etc. In-depth knowledge of benchmarking, distributed computing, parallel computing, and machine learning is required.

  • Big Data Engineer - They are responsible for creating the ecosystem that the business systems use for communicating with each other and collating data. They plan, design, and develop a big data environment on Spark and Hadoop Systems. You must have significant experience in programming languages like Python, Java, C++, Scala, etc. as well as concepts like data mining, data migration, and data visualization.

Artificial Intelligence can be described as the simulation of human intelligence by machines. This includes learning, reasoning, and self-correction. The top AI applications include speech recognition, machine vision, and expert systems. 

They can be of two types, strong or weak. Weak or Narrow AI is an AI system that is built and trained for a specific task. Virtual assistants like Siri are weak AI. Strong AI is an AI system that has human cognitive abilities. They can find a solution to an unfamiliar task without human intervention.

Here are the types of Artificial Intelligence: 

  • Reactive Machines - These are the AI systems that respond to the step of the user. For example, a chess game. When you are playing chess online against a computer, it plans it moves according to your moves. There was the IBM chess program in the 1990s that beat Garry Kasparov. It could identify the chessboard pieces and make predictions. However, it didn’t have memory and thus, was unable to make future predictions using past experiences. It analyzed possible moves of its opponent and selected the best one. Google’s AlphaGO and Deep Blue are other such responsive games that were created for narrow purposes and couldn’t be applied easily to another situation. 
  • Limited memory - This type of AI system can make informed future decisions by using past experiences. Self-driving cars an example of such AI systems. It can make observations for informing actions that might be happening in the not-so-distant future like car changing lanes. The observations are not permanently stored.
  • Theory of Mind - This is the type of AI systems that have their own desires, intentions, and beliefs. This can impact the decision they are making. This type of AI system does not exist yet.
  • Self-awareness - Such AI systems have consciousness i.e., a sense of self. They can understand their current state and use it for inferring what other people might be feeling. Such AI systems do not exist yet.

The applications of AI include speech recognition, image recognition, sentiment analysis, chatbots, and natural language generation. For these different models like neural networks, deep learning, and machine learning can be used. Also, you need to use compute platforms, GPUs, parallel processing tools, and cloud data storage platforms and have an understanding of programming languages like Python, Java, C, TensorFlow, etc. 

There are several domains in which Artificial Intelligence can be applied:

  • Marketing - In layman terms, marketing is a way to attract customers. Today, when you look for an item on an e-commerce website, you get all the items related to your search. You even get recommendations based on this product. It is all the magic of AI. Product recommendations on Amazon, movie recommendations on Netflix, playlist recommendations on Spotify, etc. are just some of the areas where AI is used for marketing.
  • Banking - Several banks have incorporated AI for providing customer support, detecting credit card frauds, and anomalies. For example, HDFC bank has EVA (Electronic Virtual Assistant), an AI-based chatbot. Since its launch, it has addressed more than 3 million queries, made more than 1 million conversations and interacted with more than 500,000 unique users.
  • Finance - There are several ventures that are using data scientists for determining future trends in the market. The ability to predict the future accurately determines trading. With the help of AI, you can analyze a large amount of data in less time. They can also observe patterns and predict future outcomes.
  • Agriculture - AI can be used by farmers to get more from land and use their resources efficiently. It is used for improving crop yield while keeping in mind the population growth, climate change and other concerns related to food security. Robotics and automation are used by farmers to protect their crops against weeds.

  • Healthcare - AI has helped patients across the globe. Organizations like Cambio Healthcare are working on a decision support system for preventing stroke. This will give a warning to the physician if the patient is at risk of having a stroke.CoalaLife is another such company that can diagnose cardiac diseases using a digitized device.

AI certifications have become an important part of your CV. If you are still wondering if you should go for AI certifications, here are a few reasons that will help you make the right decision:

  • Increased demand - More and more companies have started to incorporate AI in their decision-making process. It also allows them to provide customized instructions and solutions to employees and customers in real-time.
  • New career paths - According to a report by Gartner, by 2020, there will be 2.3 million jobs in AI. The report byCapGeministates that about 83% of all the companies using AI believe that the field is creating new jobs. Since the field requires new skills, they have added new opportunities. Here are some of the top AI roles:

1. Researcher - They work on finding improvements to algorithms of machine learning.

2. Software development, testing, and program management - They develop infrastructure and systems that are responsible for applying machine learning to the input dataset.

3. Machine learning applications - They apply AI or machine learning framework to a particular problem like applying machine learning to ad analysis, gesture recognition, fraud detection, etc.

4. Data mining and analysis - This involves investigating large datasets to create and train systems for recognizing patterns.

  • Improved Earning - The average annual salary of an AI engineer is $135,000. Several tech enterprises are hiring people with AI knowledge at a hefty salary.AI Certifications to your CV will improve your earning potential by making you more marketable.

  • Reaching the interview - A certification will help you successfully complete the other rounds and reach the interview stage. This is because you will be having all the skills required to beat the other candidates. It makes you a valuable candidate as recruiters will know that you have the required expertise and skills for the job.

Thanks to the several AI courses, you don’t have to invest years of your life studying. You can get familiar with all the principles and practices of AI, cognitive and automation systems. This will help you increase your value no matter your profession, field of business or expertise. The online courses cover the basics as well as advanced implementation of AI. Some of these courses are for absolute beginners with no prior technical expertise. Others are for professionals with a certain level of technical expertise. Here are some of the AI courses that will help you get started in the field of AI: 

1. Learn with Google AI

This online course is a part of Google’s plan of broadening AI among the public. It contains a crash course on using TensorFlow for machine learning. New content is slowly added. The course introduces you to machine learning and TensorFlow, and slowly moves on to neural networks. It is for people with zero knowledge of Machine Learning. 

2. Google - Machine Learning

It is an in-depth course provided by Google through Udacity. You need to have a basic understanding of Machine Learning to take this course and must have knowledge of supervised learning methods. You will also learn about deep learning and how you can design self-teaching systems that use big, complicated datasets. 

3. Stanford University - Machine Learning

Taught by Andrew Ng, the founder of Coursera, and head of AI for Baidu, this course is available for free for candidates who want to use AI for increasing their career prospects. It includes real-life implementation of machine learning like in enhancing web search and speech recognition. You will also cover topics like linear regression, MATLAB tutorial, backpropagation methods, etc. 

4. Columbia University - Machine Learning

This course will help you learn methods and models that can be used for designing applications that can solve real-world problems through probabilistic as well as non-probabilistic methods. This also covers supervised and unsupervised learning methods. It is offered through edX where you will go through learning materials and exercises.

Here are the best Artificial Intelligence courses that you should try for boosting your career: 

1. Machine Learning AI Certification by Stanford University

This course is taught by Andrew Ng, who is a professor at Stanford University. With more than 1,680,000 enrollments from across the globe, this is definitely the go-to course for people who want to learn Machine Learning. The course begins with an introduction to the basic concepts of AI like supervised/unsupervised learning, kernel, support vector machines, neural networks, etc. Different applications and case studies are used for helping you get hands-on experience on the concepts.

2. Artificial Intelligence Executive Certification from Kellog

This course is for professionals who want to learn the techniques and strategies of AI for solving business problems. Once the basic concepts are covered, the course moves on how to AI impacts different industries and how you can use different tools to develop efficient solutions. 

3. Deep Learning by Andrew Ng

This is an array of 5 courses that will provide you with deep learning specialization. During the course, you will learn about the core concepts of deep learning, building neural networks and leading ML projects. Also, you will be working on case studies from different real-world industries. You will be able to practice the concepts in Tensorflow and Python.

4. AI Certification by Columbia University

With this certification, you will be able to gain expertise in AI through lectures and assignments. The lectures will help you get an understanding of the principles of AI. The course emphasizes equally on theory and practical. It will teach you to deal with real-world problems through suitable solutions.

5. Introduction to Artificial Intelligence by IBM

This is an introductory course offered by IBM where you will be learning the basics of AI and how it can be used in the software development industry. During the course, you will be dealing with the issues pertaining to AI like bias, ethics, and jobs. You will have to complete a mini-project for demonstrating your knowledge of AI.

Image processing and AI are both exciting fields that are continuously growing. Image processing can be considered a part of AI which is used in making of almost all intelligent systems. You need to understand both for deciding which field to choose:

1. Image Processing

Thanks to the Smartphones and cloud storage, you can now click thousands of pictures. But before you can post any of these images, you wish to make it look thoughtful and want to tell the time the image was taken. You might also want to print the name of your lovely pet as a hashtag. For doing all these tasks, you have to use an image editing application. This app runs multiple functions in the backend. Every function is running an image processing algorithm which uses image as an input, uses algorithms for performing mathematical operations on the input, and provide the desired image as an output. All these activities fall under the category of Image processing. 

2. Artificial Intelligence

AI is the amalgamation of computer vision algorithms, image processing, and machine learning algorithms that are used for generalizing the system. They work like our brain for making any decision by taking a constant look at the things it surrounds. All these things are data and you can update the attained knowledge through experience and feedback. Now, for an AI system, you will have to use image processing algorithms for feeding the pre-processed images and telling them where the things are. Once the system has this information, computer vision comes into play. Every layer of the AI will be running a computer vision algorithm that will be extracting features from the image. The first few layers will be extracting low-level features. These extracted features will be clubbed later. The learned filters will be then used for predicting objects on the test dataset. All the learning is saved and can be used on any data.

Before you take an AI course, you must be familiar with the role you want, the skills required for the role and career options available. For a career in AI, you need to be familiar with robotics, automation, and how to use sophisticated computer programs and software. You should have specific education based on math, logic, technology, and engineering perspectives. You should also have verbal and written skills for conveying how you can use AI services and tools within industry settings. If you want to be successful and advance in your AI career, you need to have a few characteristics. You should have an analytical thought process and must be able to provide solutions that are efficient and cost-effective. You will have to keep an eye on technological innovations and state-of-the-art programs that will help you stay competitive. All the AI specialists must have the ability of designing, maintaining, and repairing software programs and technology.  


Here are the educational requirements you need to fulfill before taking up an AI course: 

  • Math that includes algebra, calculus, probability, statistics, logic, and algorithms
  • Physics, robotics, and engineering
  • Graphical modeling, bayesian networking, neural nets  
  • Cognitive science theory
  • Programming languages, coding, and computer science

Different companies provide different job roles in the field of AI. Depending on the domain on the company like healthcare, education, military, government agencies, etc., you will be handling different levels of sensitive information. Here is a list of specific jobs for AI professionals: 

  • Software Developers and Analysts
  • Algorithm Specialists
  • Electrical and manufacturing engineers
  • Computer engineers
  • Computer scientists
  • Engineering consultants
  • Research scientists
  • Maintenance technicians
  • Mechanical engineers
  • Surgical technicians with experience working with robotic tools
  • Aviation and military electricians working with armaments, drones, and flight simulators
  • Professionals working in medical health with prosthetics, artificial limbs, vision restoration devices, and hearing aids.
  • Digital musicians
  • Textile architects and manufacturers
  • Graphic art designers
  • Entertainment producers
  • Post-secondary professors working at universities, trade and technical schools, and vocational centers

AI is changing its capabilities and scope at an exponential rate. A couple of decades ago, calculators were considered AI. Today, we have gadgets and home automation systems powered by AI like Siri, Google Home, and Siri. Big brands like Google and Facebook are using AI for improving the user experience. For example, the auto-reply feature that provides suggested replies on Gmail is one of the applications of AI where the machine learns these responses.

If you want to get started with AI, you need to have a strong foundation. You can’t just finish a course and get a job in the field. You have to learn the skills required for the subject through different learning paths. You have to chart your course through your previous skill and knowledge level. You need to finish the prerequisites of AI for getting a strong foundation of all the key concepts. You must have a strong base of linear algebra, calculus, and statistics you can start working on developing algorithms. Also, you must be familiar with Python and how it is used for data science. Python is one of the most commonly used programming languages in the field of AI.

  • Before you start studying AI, you need to brush up on your math skills. There are several courses that can help you learn how to use existing libraries along with your math skills for solving a problem.
  • Next prerequisite is machine learning concepts. This will help you plan and collect data, interpret the model results and create better results.
  • You need to make sure that you have a complete understanding of data extraction, preparation, and cleaning. While working in AI, you will be applying data cleaning and feature engineering concepts on your data.
  • You should try participating in Kaggle competitions for practicing. They are easy competitions where you will be solving problems with multiple typologies and scenarios.

Here are the top reasons for learning Artificial Intelligence: 

1. It is the skill of the future

AI is the current trend. More and more companies are starting to use AI for their decision making process. But, we still don’t have enough trained AI professionals. With proficiency in AI, you can ensure a future-proof career. Since it can be applied to different markets and their problems, it is considered to be a new approach for all problems and solutions. AI can help you take advantage of unknown opportunities and make way for success. As an AI professional, you will be designing AI-based products and services, applying AI technology to real-world problems, evaluating algorithms, design software and information architecture, researching for advancing current technologies, mining and analyzing data for creating value, etc. 

2. It uses data

Today, we have so much data. AI can use this data for performing high-volume, computerized tasks without fatigue. You can use AI with the existing products it adds improved capabilities to the products. Progressive learning algorithms are used by AI for finding regularities and structure in data. This means that AI can teach a system to play chess or recommend a product online. You can use a neural network with AI for analyzing deeper data. With AI, big data and computer power, deep learning models can be trained for detecting frauds. AI can be used in the medical field for finding cancer accurately on MRIs. Basically, with AI, data can be used as intellectual property. It will give you a competitive advantage. 

3. High salary

AI professionals are some of the highest paid IT professionals. The average annual salary of an AI professional is $135,000. This makes it one of the most respected jobs in the IT industry.  

4. Others

One of the most common reasons for learning AI is that it is the dream job for someone who loves math. Since it is an emerging field, there are a lot of job opportunities for you.

If you want to make a successful career in the field of AI, apart from the specified skills and educational requirements, you will have to follow a path: 

1. Online courses - You don’t have to spend years in college to become an expert in AI. Just like other tech-based fields, there are online courses for robotics and AI. Whether you want to learn about the complete field or about a specific topic, you can find everything online.

2. Study - AI is an ever-changing field that is evolving constantly. So, it is important that you read and research for keeping yourself informed of all the technological innovations. For this, you can try exploring learning opportunities and subscribing to scientific journals.

3. Problem solving - You must practice learning how to use R or Python for problem solving. Also, you must know the application of AI for solving a problem. You need to apply craftsmanship and imagination to your code for developing a solution successfully.

Here are the different roles you can take after following the above-mentioned path:

1. Machine Learning Engineer

They are computer programmers who are responsible for applying complicated predictive models, processing large datasets, and using NLP for programming machines to perform tasks that are supporting the goals of a business. 

2. Data Scientist

A data scientist is responsible for analyzing and visualizing large data for building and implementing machine learning models to support the decisions of the business. For this, you need knowledge of programming and statistical computing languages. 

3. Business Intelligence Developer

They research and plan solutions for business and analyze complex data for increasing profitability. They design, model, test, and maintain data storage systems and analyze the data to check for trends. This improves the profitability of the business. 

4. Software Developer

This job is for the one who guides and supervises the development process of computer software and programs for the business. They focus on machine learning and AI and should be an expert in coding, building and optimizing complicated systems. 

5. Robotics Scientist

They are responsible for building and maintaining the robots that are carrying out those tasks in an organization that need some human input. They work in sectors like manufacturing, healthcare, space, security, etc.

With an AI course, you will be able to get all the skills required to get a job in the field of AI. For a career in AI, you must have knowledge of robotics, automation, and using computer programs and software. A strong foundation of math, engineering, technology, and logic is required. For acquiring these skills, you can either go to college or take up an AI course. 

All the successful AI professionals have some shared characteristics that allow them to advance in their career. This include: 

  • Analytical thought process
  • Finding efficient and cost-effective solutions
  • Foresight regarding technology innovations
  • Knowledge of the best programs in the field of AI
  • Technical skills for designing, maintaining, and repairing software programs and technology
  • Converting technical information into a language that is easy to understand by the non-technical members of the team

The education requirements for an AI job that you can fulfill through an AI related course include: 

  • Math which includes algebra, calculus, probability, statistics, logic, and algorithms
  • Cognitive science theory
  • Physics, robotics, and engineering
  • Neural nets, graphical modeling, bayesian networking 
  • Coding and computer science

With all the above mentioned skills, you can look for a job as an AI professional, in roles that include: 

  • Algorithm Specialists
  • Aviation and military electricians working with armaments, drones, and flight simulators
  • Computer engineers
  • Computer scientists
  • Digital musicians
  • Electrical and manufacturing engineers
  • Engineering consultants
  • Entertainment producers
  • Graphic art designers
  • Maintenance technicians
  • Mechanical engineers
  • Post-secondary professors working at universities, trade and technical schools, and vocational centers
  • Professionals working in medical health with prosthetics, artificial limbs, vision restoration devices, and hearing aids.
  • Research scientists
  • Software Developers and Analysts
  • Surgical technicians with experience working with robotic tools
  • Textile architects and manufacturers

Over the recent years, there has been an exponential growth in career opportunities in the field of artificial intelligence. This is primarily because of the increasing demand of various industries which have been digitally transformed. While the number of jobs available in the field is ample, there is currently a shortage of talented individuals with the necessary skills.

Just like in the rest of the world, new AI jobs are opening up in India almost on a daily basis. Any average AI professional with 3 years of experience will need to upskill for keeping up with this demand. Some key industry sectors offering jobs for AI engineers are:

  • Medicine: This includes interpretation of medical images, expert systems for aiding GPs, diagnosis, monitoring, control in ICUs, design of drugs and design of prosthetics
  • Robotics: This includes motor control, vision, learning, linguistic communication, planning, and cooperative behavior.
  • Engineering: This includes intelligent control systems, fault diagnosis, intelligent manufacturing systems, integrated systems for sales, intelligent design aids, production, design, maintenance, and expert configuration tools.
  • Information Management: It involves the use of AI in web crawling, email filtering, data mining, etc. For example, a California company makes use of AI for helping retailers with mining of customer data, by examining the ages, buying habits and postcodes of people purchasing goods over the internet. Google uses applied artificial intelligence platforms for learning
  • Space: Autonomous robots and space vehicles are located too far from the earth to be directly controlled and manipulated by people on earth due to transmission delays. NASA makes use of AI for aiding the planning and scheduling of space shuttle maintenance.
  • Military Activities: Perhaps, this is the sector in which the most money has been spent. Due to the nature of activities, not a lot is known about the details.
  • Marketing: The use of AI helps in developing more timely, relevant and targeted marketing programmes for increasing rates of customer attrition.

Artificial intelligence is currently one of the most attractive and exciting fields to get into. The growth of the global machine learning market is expected to increase to $8.8 billion by 2022. According to recent reports, it is expected that 2.30 million jobs will be created related to AI by 2020. Not just that, AI jobs offer lucrative income as well. Top talent can easily attract big companies and get paid handsomely. Clearly, there are plenty of reasons to look for a job in the booming industry. 

There is no denying that AI is where the future of the world lies. Not just the big names like Alibaba and Amazon, even the small start-ups are putting their focus on AI. Algorithms have become essential these days, as they can help businesses impress their consumers, customers and investors. Some exciting job opportunities in the field of artificial intelligence include: 

  • Data Scientist: A job in data science involves analyzing visualizing and modeling large amounts of data related to a service or product that is being sold by a company.
  • Machine Learning Engineer: A machine learning engineer is a computer programmer who programs machines for performing particular tasks.
  • Software Developer: A software developer writes the software for supporting the deployment and development of AI and machine learning systems.
  • Robotics Scientist: Professionals in this field are responsible for making products like Roomba and Alexa. They work for years to develop a product to make sure the robot is functional
  • Business Intelligence Developer: These professionals essentially serve as data scientists, although their focus is primarily to ensure more comprehensive data for businesses.
  • AI Research Scientist: A professional in this field specialize in AI subjects like applied mathematics, machine learning, and computational statistics. They have the responsibility of applying machine learning and AI for achieving different goals of a business.

The paycheck of professionals in the field of AI is usually high and there’s a reason for that. The high salaries are a direct result of scarce talent available and extreme demand for that talent. This is the age-old relation between demand and supply and currently, AI professionals are in very high demand.

While the average salary for an AI professional is relatively high, it still depends on the sector you work in. AI jobs do remain to be plentiful, but they are still limited to just a few sectors, primarily the tech sector. And job opportunities are only available in some of the big and expensive cities.

As of 2018, the 5 best AI companies were Facebook, Adobe, NVIDIA, Microsoft, Accenture and Uber. These companies comprised of nearly 19% of the total available AI positions. As per Glassdoor, the average annual base salary for a job in AI is $111,118 per year. Not just the big companies, even the financial services, government and consulting agencies are also actively recruiting data science and AI engineering professionals.

Artificial Intelligence itself is a broad field and it includes a number of tasks and disciplines that includes natural language comprehension and generation, speech recognition, machine learning, chatbots, decision management, biometrics, deep learning and text processing and analysis. Each discipline requires a certain level of specialization. That is why it is hard for a professional to have expertise in more than a single discipline.

Contrary to software development, AI is not a discipline that can be self-taught. A huge majority of developers are actually self-taught. That is not the case with artificial intelligence, as it often requires an advanced educational background, commonly a PhD. Not just that, it requires multiple disciplines, including C++, Python, Perforce, APIs like PhysX and OpenGL, etc. With such a varied skill requirement, it is no surprise that AI offers high salaries.

There is no doubt that Artificial Intelligence is one of the advanced technologies that is predominant in the current marketplace. AI has the potential of transforming the way a business operates and the interaction between humans for performing complex tasks. 

To pursue a job in the field of AI, you need an advanced degree in Computer Science. Even professional engineers with a BS in Computer Science have the opportunity of switching to the field with some experience. However, the challenge remains to make the switch in a natural and smooth manner. Here are some major skills that are a necessity to become an Artificial Intelligence Engineer: 

  • Proper knowledge of algorithms and mathematics: Ideally, a candidate in the domain of AI needs to have the expertise in applied mathematics and algorithms. The candidates need to have good analytical and problem-solving skills to be able to perform tasks efficiently.
  • A good grasp of statistics and probability: A thorough understanding of statistics and probability is required for understanding the different models of AI like Hidden Markov Models, Gaussian Mixture Models, Naïve Bayes, etc.
  • Basic programming expertise: AI Engineers need to learn languages like C++, Python, Java, R, etc. for becoming proficient in ML and AI. Python helps in creating complex algorithms and C++ helps in speeding up the coding process.
  • Distributed computing: With most of the AI jobs, programmers have to deal with large sets of data that cannot be processed with a single machine. Distributed computing is required for equally distributing data sets across a cluster.  
  • Unix Tools: Majority of AI processing is done on Linux machines; hence programmers should have a basic knowledge of Unix tools like awk, cat, grep, find, cut, sort, etc.
  • Creativity and curious: While not a skill that can be considered a prerequisite, a creative and curious mindset definitely allows professionals to excel in the field of AI.

Those who run businesses are always looking for ways to improve performance and save money. These objectives are the main reason behind the technological advancements in the field of artificial intelligence. AI is nothing but the use of computer technology to perform tasks that usually need human intelligence to be performed. There is no computer system that completely simulates human behaviour. However, there are several programs that are designed for making decisions and taking actions based on input. 

Initially, AI was just a research subject for selective doctoral candidates. Now, it has varied applications for government agencies, commercial and customer use. AI is used for creating expert systems capable of gathering data, processing it into information and intelligently responding to it. Such technology has great application in healthcare, agriculture and even military defense systems. The field of robotics makes use of AI for designing and developing machines capable of working in places not suitable for humans, like outer space and great depths. AI technology can also be used for learning a foreign language without a human tutor. 

Most professionals working in the field of AI have an undergraduate degree in computer science along with advanced AI degree. There is a variety of coursework AI professionals can potentially undertake, including course subjects such as programming, linguistics, math, statistics and even philosophy. AI professionals need to have problem-solving, analytical, logic and communication skills in order to be successful. 

There is a high demand for skilled professionals in the field of AI all over the world. The salaries and availability of jobs depend on the specific AI specialization along with educational background and experience level of the candidate. Candidates with undergraduate degrees can go for the entry level positions, while those with graduate and doctoral degrees in AI get the best jobs in the industry. 

The salaries of Artificial Intelligence Engineers are broadly influenced by the fact that the field is trending, the demand for skills is high and top talents are scarcely available. As such, anything related to the field of AI continues to be in high demand.

The entry level salary for artificial intelligence can depend on your credentials. As per Indeed.com, the starting IT salary for Artificial Intelligence Engineer is around $134,135 annually in San Francisco for Software Engineer and it goes as high as $169,930 annual for a Machine Learning Engineer. The starting salary can indeed go even higher if you have the credentials the firm is looking for. In fact, a tenured professor was offered three times his salary of $180,000 for joining Google. Interestingly, he declined the job offer for pursuing another teaching position.

Apart from your credentials, the sector of your choice is also influential on your starting salary. AI jobs may be plentiful, but they are mainly available in the technological sector in some of the major cities of the world. A popular job search website, Glassdoor, reported that 67% of all Artificial Intelligence jobs listed on its website are in the location of Bay Area, New York City, Los Angeles, Seattle, etc.

Big companies like Facebook, Uber and NVIDIA account for a majority of AI job opportunities available. Other than that, government agencies, financial services and consulting agencies have also started hiring data science professionals and AI engineers. Some of them include firms such as Capital One, Goldman Sachs, Fidelity, EY, Booz Allen Hamilton, and McKinsey & Company, the Federal Reserve Bank, NASA’s Jet Propulsion Laboratory, and the U.S. Army.

The number of job opportunities across different sectors is expected to grow considerably in the future. It is reported to create as many as 2.3 million new job opportunities by 2020, which is surely going to impact the entry level salary for AI Engineers.

The field of Artificial Intelligence is a rather broad one. It covers a variety of disciples and tasks, including speech recognition, natural language generation and comprehension, chatbots, decision management, machine learning, deep learning, text analysis and processing and biometrics. The average annual base pay for a job in AI is $111,118 per year, as listed on Glassdoor. However, the average salary depends on the level of specialization an individual has.

To fetch the best salary in the field of AI, you need to find the right job. Artificial Intelligence Engineer is one of the highest paying career paths. Becoming a good in-demand AI engineer requires you to have a lot of skills, which include machine learning, data science, Hadoop, Big Data, Python, R, and Java language.

For example, the average salary for an AI programmer ranges from $100,000 to $150,000, based on the location and the role is mainly concerned with coding and developing. In comparison to that, an AI engineer earns much more. The average salary for AI engineer, as per Paysa, is $171,715, and it ranges from $124,542 at the 25th percentile to $201,853 at the 75th percentile. The highest earner in the domain gets paid over $257,530.

So, why is the average salary of an AI engineer so much higher in comparison to other jobs? This is because professionals in other jobs do not have a programming background. In fact, people having PhDs in sciences like physics and biology are going back to school to learn AI and apply to their field. They need to get used to the technical aspect of it. They need to learn about the hardware architectures along with several programming languages. An understanding of data is a must. AI engineers possess such knowledge, which is why they are rare and have high salaries.

In the USA, the average salary for Artificial Intelligence Engineers starts at $50,014 per year for entry level positions. The average salary remains $155,500 annually, estimating to $79.74 per hour, with the most experienced workers earning up to $233,600 annually.

Artificial Intelligence is the cutting-edge technology that continues to increase in popularity and it seems to be a decisive technology for the future as well. The capabilities of AI are amazing and can be effectively used in nearly all industries. But, how do you get from being a passive observer of its capabilities to being in the front seats?

You would assume that having a successful career in the field of AI is a difficult and tedious task. That might be true to an extent but there’s no reason why you can’t actually do it. As a matter of fact, even someone who doesn’t have any prior experience in math, engineering and programming can start learning AI from scratch. To do that, you would obviously want to get the right training.

1. Start with the basics: Learning AI can be a complicated process so it is essential to approach it correctly. You have to begin with some basic skills. First and foremost, you should be capable of abstract thinking. Good logical reasoning and problem-solving skills are a top priority these days. You also need to be proficient in mathematics, statistics, algorithms and even physics if you wish to go into robotics. There are many online resources and training programs in each domain.

2. Learn AI: This is the most important and interesting step. As a part of your training, you should be looking to learn:

  • Computer Science and Programming: Dealing with computer science-based applications is a major part of an AI professional’s job. An understanding of Python is a must.
  • Learn AI: AI itself is a broad field of study and some of the basic concepts you should get training on include:
  • Machine Learning
  • Neural Network
  • Cognitive Computing
  • Deep Learning
  • Computer Vision

With devices like Oculus Rift and Gear VR getting popular, virtual reality has recently come into the spotlight. VR training techniques have actually been used for a while now by schools, hospitals, companies, and even the military. This is the type of training that places the trainee in a 3D immersive environment, related to the subject in question.

The biggest advantage of such training is that it gets rid of all distractions, allowing for complete immersion of the trainee in the learning environment. It allows the trainee to respond to the environment simulating real life situations. What it means is that the trainee not only acquires information relevant to their job but also get a practice of those skills from the beginning itself.

Augmented Reality and Virtual Reality have conventionally been preferred for hands-on training across the world. For instance, they are used in the hospitality industries to train service staff on completing their duties properly. However, these are hard skills and AR and VR weren’t considered as viable options for soft skills training until recently.

With the help of VR, common workplace scenarios can be recreated, allowing employees to get used to these situations. 360-degree videos can be used to allow employees to explore different environments. An interactive and immersive environment can be created, with feedback being received and incorporated immediately. In this case, the digital assets provide response to trainee behaviour, course correction and signaling errors.

AR differs slightly as it doesn’t recreate the complete ecosystem. Instead, digital objects are superimposed into the current surroundings. One use of AR in soft skills training is the use of AR systems by sales professionals for practicing selling techniques.

Incorporating VR and AR along with AI in Training, Learning and Development opens up a lot of possibilities for practical workplace examples to train with.

Corporate training has for a long time revolved around the conventional Learning Management Systems. Such training method is actually functional for formal and compliance training. However, it does nothing to take the experience to the next level. In fact, there is a notable drop in learning rates. The advent of new technologies and wider skill gaps have disrupted corporate Learning & Development. It is believed that artificial intelligence will have a positive impact on corporate training and learning. 

AI has actually been more prevalent than you would think. From the context of employee engagement and recruitment, AI has been incorporated by different business sectors for improving KPIs and productivity. 

The flow of learning is what needs to be focused on than simply the instructions. AI allows employees learn things in the most natural way possible. This can be done with the integration of employee training into the workflow or keeping track of the workplace behaviour of each employee for personalizing the training content. With AI, L&D platforms can be more automated, personalized and measurable. It helps by: 

  • Personalizing learning pathways: A single training approach for everyone isn’t the most ideal one. The better way to approach is to provide a different learning experience to each employee based on factors like job role, previous learning experience, learning style and educational background. AI is the perfect technology to use for achieving such learning methodology.
  • Improve completion rate: AI can be used to make learning content more personalized, relevant and concise, thus playing a huge role in improving the completion rate.
  • Content at scale: For businesses that are agile and have dynamic requirements, it is important to create training content at scale and AI can help achieve that.
  • Human interaction on focus: Skill requirements are rapidly changing, resulting in the need to conduct more frequent training sessions. AI can be used for creating virtual mentoring tools for automating repetitive tasks like content creation and analysis.

Artificial Intelligence continues to evolve and there is no denying that the technology will be playing a huge role in the future. All the big companies across the world know this fact, which is why they are heavily investing in AI. Major names like Google, Amazon, Microsoft and Apple have dedicated resources to AI. 

AI is rapidly changing every aspect of human lives, and the professional aspect of it is no different in this regard. Even those who don’t work for technology companies will soon witness the presence of AI enabled machines in their day-to-day work life. AI technology will become increasingly prominent in recruiting, on-boarding, training, personal development and even passing the skills and experience to those who follow. 

Even before a person sets foot in a new workplace, AI could have already played a role in ensuring that person was the right one for the job. Pre-screening of candidates with the help of AI to find the most suitable ones to call for interviews is a common practice among large companies. It is responsible for attracting millions of applicants and making thousands of hires. 

Pymetrics provides tools that make use of neuroscience principles for assessing candidates before interview. AI-based chatbots is another preferred tool for onboarding. Unilever, a multinational consumer goods manufacturer uses a chatbot known as Unabot for employing NLP for answering employee questions in human language. 

Learning doesn’t stop once a professional settles into the new role. AI technology can play a role in ongoing training of employees. Honeywell, an engineering giant has developed tools utilizing virtual and augmented reality along with AI for capturing workplace experience and extracting lessons that can be passed on to new recruits. 

AI or Artificial Intelligence has been an important and growing industry adopted by IT companies and other sectors alike. Here are some of the top AI platforms in the world known for their top-notch services and amazing state-of-art technologies.

Amazon

Amazon is one of the biggest trade platforms on the internet today that has usurped a major chunk of the online traffic and customer base online. The company has also ventured into other sectors and is investing heavily in technology, particularly AI tech. Alexa, the AI designed framework by Amazon comes with an integrated speaker and voice recognition software.

Apple

Apple has been upgrading its AI tech game for a few years now, introducing new platforms for its customers. Siri, the AI platform by Apple has been a part of all of its tech gadgets. IOS users can now access Siri on their phones, laptops, tabs and other devices as well.

Banjo

Banjo was an IT service company that was established after the bombings at the Boston Marathon in 2013. The start-up has developed an AI app that tracks and traces social media to report and monitor current events from around the world and broadcast it online. It provides accurate and quick updates of international events and is a useful tool for dispatching immediate help or other emergency services.

Google

Google has been around for some time now and has a huge online presence. However, the company is not just limited to being a search engine. It has spread its roots in almost every sector from cloud services to email marketing, live streaming, and even artificial intelligence. Google Assistant is a new and unique AI software that runs on phones, laptops, and other devices. It can be integrated into any platform and helps users keep track of their Google accounts, make calls, send messages and manage other things efficiently. 

Artificial Intelligence is a sector that is growing by leaps and bounds. Everyone wants a piece of AI tech for their company. Therefore, it shouldn’t come as a surprise when you find AI developers and machine learning experts being one of the most coveted and in-demand professionals of the IT sector. All the top organizations want to hire the best of AI experts for their team. If you are trained in AI then these are some of the companies that you can apply for. Note that there are several other companies looking for AI trainees and freshers. A quick online search will give you a list of the best opportunities in your country. 

  • Google
  • Amazon
  • Apple
  • Microsoft
  • DJI

These companies are some of the biggest ventures in the world that offer a gamut of opportunities and lucrative salary packages to AI designers. As an entry-level AI engineer, you will have to work with the algorithms of the system and monitor the database. Engineers can choose from a wide range of projects and programs that interest them to further upgrade their skills and gain more experience in the industry.

Most of these companies have their own AI applications and extensive database management systems that need to be tracked, fixed, updated and customized on a regular basis. AI technicians deal with all that and more.

AI professionals are required to work ina number of sectors from marketing to finance and even hospitality. You have to, therefore, be equipped to build apps, web pages, have great coding skills and a thorough knowledge of data science and machine learning as well if you want to succeed as an AI engineer. Based on your academic credentials, technical skills, and industry experience, you can easily make about $100000 and above per year by working on artificial intelligence-related projects.

AI is a field that requires developers and engineers to work with huge volumes of data. In such a taxing job, it is mandatory that you have some killer maths skills and a flair for handling numbers. Mathematics and stats are an indispensable part of machine learning, data science, and AI platforms. In-depth knowledge of linear algebra, probability, multivariate calculus, and data optimization courses are a mandatory part of your curriculum. Here are some of the popular topics that are frequently used in AI jobs

  • Vectors and matrices: students of AI should understand concepts like scalar multiplication, innerproduct(dot product), vector projection, cosine similarity, orthogonal vectors, normal and orthonormal vectors, vector norm, vector space, linear combination, linear span, linear independence, basis vectors
  • Principles of components like eigenvalues and eigenvectors
  • The basic and advanced rules of probability
  • Functions, derivatives, and gradients of calculus theories
  • Other theories like entropy, cross-entropy, KL divergence and implementation of Markov chain

With good knowledge and practice of these aforementioned math skills, you can easily work with the complicated algorithms, make your own apps and integrate multiple functionalities with your platform with relative ease. That being said, do not assume that the mathematics syllabus in AI is limited to but these few topics. You need to keep yourself updated on the latest principles and trends of the sector to know about the skills that are most in-demand at present.

AI is a field that requires constant work and practice. You need to always keep up with the changing times and understand the concepts that are most in vogue in the industry. Also, it is important that you harbor a passion for data and love working with huge volumes of information as the workload can get very exhaustive and extensive here.

Artificial Intelligence is one of the few fields that has managed to gain such an extensive and expansive following and popularity in such a short span of time. The cutting edge technology when integrated with business platforms can take your enterprise to a whole new level. Ecommerce platforms and other organizations are investing heavily in AI frameworks to offer consumers a more customized, personalized and humane experience without compromising on the accuracy and precision of its services.

If you are a developer or someone interested in the IT sector then a career in AI is one of the most profitable and enriching experiences that you can have. AI is in demand and engineers get the freedom to work on whatever tech they want.You can choose your own projects, and you get paid handsomely for it as well. With AI, developers can really get that boost in their coding skills. Here are some important tips for developers who aspire to enter the AI field:

  • Start with honing your basic coding skills and mathematical knowledge
  • Develop excellent analytical and problem-solving skills, practice abstract thinking
  • Master topics like calculus, probability, and linear theory
  • Also, have in-depth knowledge of data science and machine learning as you might have to work with complex hardware and databases
  • If you are into robotics then knowledge and interest in physics and mechanical engineering is crucial
  • Master coding platforms and cloud-based frameworks
  • Last but not the least you must learn how to work with, manage and track algorithms

To get these skills you can either take up online courses or join an academic institution. Look for a platform that offers professional degrees and certification as it can be a huge addition to your CV. Finally, you must always be ready to upgrade your skills, learn new theories and put them to practice if you really want to become a successful AI developer

AI or artificial intelligence has revolutionized the way people live, commute and communicate these days. The technology involves working with huge volumes of data and can be implemented in several sectors like banks, financial institutes, hospitals, schools, tech companies, electronics, etc.

Artificial Intelligence offers immense scope for growth and development to developers, technical experts, and engineers. You can integrate the tech with cloud-based platforms and extensive databases to form new applications and perform complicated tasks quickly and with relative ease.

There are different types of AI frameworks that you can work with. Strong AI represents platforms that are designed in a way that appears to be human or extremely realistic. These apps are developed with the capacity to think for themselves as people do. Engineers are working with AI principles, integrating them with robotics to create nanobots and AI neural networks that can now fight diseases, find solutions and even empathize to some extent with people.

Weak AI platforms have a narrower scope and are usually more prevalent in the market. Unlike strong AI, the weaker ones are not designed to work as a human mind. Here, the AI platform is developed to act in an intelligent way. This technology is used for designing role-playing games and interactive applications.

The latest developments in the AI sector aims at creating a framework that would surpass human cognitive intelligence. These AI platforms would then be capable of existing as independent entities with the ability to think, feel and act in a way they want. Artificial intelligence can make all the whimsical science fiction tropes possible, allowing mankind to reach new heights and increase our chances of survival. Of course, these possibilities are now a distant dream, although not an impossible one.

Contrary to popular belief, AI developers and engineers do not have it easy. In fact, their jobs are probably some of the most taxing career choices in the world that comewith a lot of responsibility. AI tech experts create apps and platforms that form the core or fundamental base upon which a business can grow. These apps help enhance the way organizations interact with customers, giving you opportunities to maximize your profits and get more business. 

The standard responsibilities of AI professionals are to maintain databases, create algorithms, get market insights and figure out ways and means to solve tech issues in the company. Here are some of the key functions of an AI engineer; 

  • Data collection: AI trainees have to be well versed in handling, sorting and collecting raw information and converting it into processed data. The stats or figures collected from surveys and different department sources are accumulated, categorized, tagged and then stored in their respective locations.  
  • Data Analysis: AI engineers should have an idea about how the market works and what are the latest trends that are prevalent among customers these days. This information can be collected from customer reviews, feedback, ratings, survey websites, and other sources. The AI developer has tothen analyze these stats to figure out trends and preferences that are most relevant to the organization  
  • Literature review: The team of data scientists also have to read through hundreds of whitepapers, research papers, journals, and other things every day and review them according to their relevance and usefulness. 
  • Model and software development: AI developers also have to work with coding platforms like Python and database management systems to source the right algorithms for the AI framework. These AI-based apps are intuitive, interactive and compatible with mobile devices and desktops alike.

AI developers get to work with some of the most interesting projects and platforms in the industry. AI tech allows you to explore and expand your technical skills, delve deeper into the world of programming and design frameworks that compute and communicate with users in a hassle-free and precise way. Most of the modern-day AI projects are based on super artificial intelligence and robotics where developers are looking for new ways to integrate cognitive thinking and emotive abilities in machines. Some of the notable projects that are currently doing the rounds in the industry are; 

  • CALO is a DARPA-funded project that integrated numerous technologies like natural language processing, speech recognition, machine vision, probabilistic logic, planning, reasoning, and many forms of machine learning to design an AI assistant that acts as a personal secretary 
  • CoJACKinspired from the ACT-R framework, allows users to elicit a more realistic and human-like response from virtual platforms 
  • Google Now is an intuitive and intelligent personal assistant that now comes with a voice interface and can be optimized for Android and Apple devices
  • Microsoft Cortana is designed for Microsoft Windows 10 and higher versions. It is an intelligent AI assistant used for managing different tasks both online and offline
  • Siri, an intelligent personal assistant that comes with voice recognition, allows users to multitask on their Apple and IOS devices 
  • Blue Brain Project is an attempt to recreate the human brain virtually using the reverse-engineering process to trace the molecular structure of mammals
  • GoogleBrain  is an extensive AI platform that allows users to run deep learning projects, recreate human intelligence and even generate a sense of empathy and realistic emotion on a virtual scale  

Most of these projects are funded by either the government or big corporations and entrepreneurs. AI developers can have their pick from numerous projects, the details of which are published and updated online. 

Artificial Intelligence is one of those few topics in the IT sector that requires candidates to be well versed in both theory and practical aspects of the program. You need to be well versed in a number of platforms, learn about the basic principles of probability, linear maths and calculus and have an in-depth idea about machine learning as well. AI is an extensive field that involves a gamut of topics and sections that you need to cover and master before you can call yourself a successful developer. If you are an amateur in the subject or have some entry-level knowledge about AI, here is a list of books that will help you improve your understanding of the subject

Make Your Own Neural Network by Tariq Rashid

This is a fun read that will take you through a systematic step by step journey on how to understand and practice the various mathematical calculations and develop neural networks using AI. The book is divided into 2 parts, one talking about the mathematical theory and the other discussing the practical aspects of the subject. You even get to practice on data sets that you can run and check the efficiency of your program with.

Deep Learning

Deep Learning is part of the adaptive computation and machine learning series that aims at decoding and simplifying the various aspects of AI using real-life examples. It even offers some math tips and calculation tricks to help students solve complicated problems of linear algebra, calculus, and probability related questions quickly and accurately

Artificial Intelligence: A Modern Approach

This book by Stuart Russell is great for beginners who want to know everything about AI from scratch. It covers the basic concepts of artificial intelligence- from what it is to where it is used and the scope it offers developers and ventures. The book is usually included in the curriculum of undergrad and postgrad students of machine learning and artificial intelligence classes.

There was a time when computers were the size of a room and took hours to calculate the simplest of problems. With the advancements in technology, developers have been working on new and innovative ways to make machines smarter and more intuitive. Earlier, computers would run on the GIGO principle, this stated that the machine could produce only what was fed to it- garbage in, garbage out. Today, thanks to AI, we have machines that have human-like abilities to think for themselves and even feel emotions.

The earliest of the AI platforms were designed to only act as an intelligent entity. These apps would interact with the user but use only generic responses that were pre-fed into their systems. The advanced AI platforms today not only learn and adapt to their users but also upgrade their database and incorporate the very personality traits and behaviors of their owners.

AI assistants like Siri and Alexa are designed to help their users manage multiple tasks like make phone calls, send messages, save pictures, send emails and other duties. Artificial assistant platforms have become a lot faster, intuitive and efficient in learning new things. Soon there will be a day when AI would surpass the genius of man and even start developing platforms on its own.

Researchers and AI developers are working on ways to upgrade their platforms using unique technologies that would bring AI’s at par with the human brain. In a few years down the road, we can predict AI to be part of almost every industry in the market, revolutionizing the way we communicate, commute and conduct business.

Artificial Intelligence is a part of computer science and information technology that deals with creating platforms that improve the computational power of machines. AI works closely with other areas like Machine Learning and Data Science helping programmers and engineers create frameworks that are not just efficient and accurate but also intuitive, interactive and realistic.

AI seems to be a complex idea, but it is surprisingly simple once you get the hang of it. The trick is to get off on the right start. The best way to begin learning AI is, to begin with, the basics and then slowly work your way up as you advance. Everything from the fundamental concepts of computers to programming to maths and even stats have to be explained and practiced to perfection.

AI has always been in demand ever since its conception and has helped simplify a lot of functions that were otherwise very time-intensive and expensive. Learning AI also improves your chances of getting a good job in the IT sector. All the major industries and sectors are now vying for AI to be integrated into their business models.

AI helps machines get more expressive and emotive. If you want to pursue a career in AI we would suggest that you learn about the basics of human psychology, develop your problem-solving skills and foster an analytical bent of mind as well.

Beginners can commence their AI lessons either online or offline. There are reliable online platforms and courses affiliated with credible universities and institutions that offer degree courses on AI and ML to candidates. Everything from the study material to the data sets and projects will be provided to you. Also, you can put in a few hours of study every day to listen to live lectures. These courses are short-term and a great way for AI engineers to upgrade their skills. 

Coding and software programming is an integral part of the artificial intelligence curriculum. For here, the developers don’t just have to deal with huge data sets but also build a suitable interface that will help compute, analyze and store these datasets in an effective and organized way. Here are some of the best coding platforms that every artificial intelligence engineer has to master;

Python

Python is perhaps one of the most important yet the easiest of programming languages that almost every developer is well versed in. The syntax is comprehensive, customizable and easy to understand, the interface is robust and user-friendly and the execution is smooth and hassle-free.

This platform runs on Object-Oriented framework and runs a lot faster than Java, C++ OR Ruby. It is also a crucial part of machine learning. 

R Programming

R Programming allows for more flexibility than most other programming platforms out there. It creates a suitable environment for analyzing, integrating, tracking, tracing and monitoring huge volumes of statistical data. Users can also insert mathematical functions and symbols in the syntax. The interface is intuitive, easy to read and simple enough to master. It also offers various packages, classes, and fields that allow you to develop and tweak algorithms to suit your requirements. 

Java

Java is a popular and well-known programming platform that allows users to build separate classes and fields, set parameters and execute them flawlessly. The object-oriented environment is suitable for artificial intelligence, machine learning, and data science platforms alike. Java also comes with regular updates, offers useful packages and comes with a host of other functionalities that would definitely boost your project. 

There are several other platforms like C++, Lisp, and Prolog, etc. that are preferred by developers that you can check out

Both artificial intelligence and machine learning are two important aspects of computer science that have helped in the major advancements of technology that we see today. Artificial intelligence is an app or software or platform that is artificially created and which can express and experience human-like impressions. This means that with AI, you can program a machine to think, act and feel in a realistic way. This would open new avenues for engineers, creating opportunities that we couldn’t even think of a few years earlier.

Machine learning or deep learning is based on somewhat the same principle and can get quite confusing for a novice to differentiate between the two. The basic difference between AI and machine learning is that AI deals with the software aspect of computer science while machine learning is slightly more hardware-oriented. However, that being said both AI and Machine learning are but two parts of the same coin. It is only when you learn and execute the concepts together that you can reach a satisfying conclusion.

Machine learning aims at improving the accuracy of the program, AI has more long-term goals that care about the end results and have little to do with precision. AI is a slightly more abstract concept when compared to ML which is rooted in practicality and logic. Also, AI tries to create human-like reactions in machines, designing them in a way that makes them learn facts faster and adapt to their surroundings. ML tries to maximize the results, boost productivity and improve the computational skills of the machine.

A simple way to understand the difference between AI and ML- ML will give you all the results of a particular problem whether it is effective and realistic or not. AI, on the other hand, has the ability to gauge and analyze each of these solutions for its own merit and show only those results that best resolve the issue.

Working with AI platforms is always an enriching and challenging experience. The interface is dynamic, allowing developers to make robust and intuitive changes to the system in a way that maximizes productivity and reduces errors. However, if you are an entry-level developer looking for ways to begin with AI, things might seem a bit confusing for you.

To get started, you need to learn all the basics of computer networking and machine learning. AI is but an extension of computing. You also need to upgrade your problem solving skills, analytical powers and mathematical knowledge base as it can really help you predict the trends and get great insights on how you would want to develop your platform. Topics like probability, linear calculation, calculus and other theories 

AI is just not all technical and theory though. The field has enough to do with practice, reasoning and experience as well. You will have to practice and execute your algorithms, work with expansive database management systems, draw up huge volumes of coding and manage multiple data sets as well.

AI also involves a lot of coding, so it would be useful if AI developers were well-versed in platforms like Python, Hadoop, Java, C++ and R programming. Basic knowledge of machine learning and data science can also be a huge bonus for AI engineers as it helps them understand the operations in a more comprehensive way.

There are multiple platforms and online tutorials where you can learn all about the concepts and even put them to use. Look up for these online platforms and read through the student reviews to get a better idea on which course would be best suited to your needs. Also, you can get some great books online as well on the AI-related topics that you can explore.

When it comes to the world of programming, Artificial Intelligence is where the future lies. Witnessing the increasing demand for AI technologies, more and more developers are looking to become more familiar with the science. When it comes to learning AI and implementing it in programming, the use of languages, frameworks and libraries is one of the first aspects that needs to be considered. Here are the top AI libraries and frameworks every programmer should know: 

1.Tensor Flow: Google’s TensorFlow is one of the first frameworks you will come across when you get into AI. It is an open-source software used to carry out numerical computations with the use of data flow graphs. The framework has an architecture that allows computation or any GPU or CPU, be servers, desktops or even mobile devices. It is available in Python programming language, which is an advantage since Python is easy to learn. However, it does affect its speed since Python isn’t one of the fastest languages. 

2.Torch: This GPU-oriented open-source AI computing framework works on LuaJIT programming language. There abundant documentation and support available for the framework. It is used by Facebook, Twitter, Google, NYU and Purdue.  

3.Theano: This Python library is designed for deep learning. The tool can be used for defining and evaluating mathematical expressions including multi-dimensional arrays. The tool is GPU optimized and comes with features like NumPy integration, symbolic differentiation and dynamic code generation. The tool needs to be used with other libraries like Keras, Blocks and Lasagne for achieving a higher level of abstraction.  

4.Microsoft CNTK: The Computational Network ToolKit library from Microsoft enhances the maintenance and modularization of separating computation networks, providing model descriptions and learning algorithms. 

5.Caffe: This machine learning framework was created originally by Yangqing Jia as his PhD project and it is currently one of the most popular frameworks. 

If you are into technology, you would have heard about artificial intelligence. There are claims that AI will change lifestyles, including the working scenario. If you are intrigued by the technology and want to be part of the transformation that AI will bring, there is no need to wait for a professor or a mentor or a manager to tell you to learn about it. You can get out there and master it! The great thing about learning the skill of AI is that not only will it give more opportunities, it will also improve your perspective around the future of business and work. You may think pursuing AI is as simple as joining some university and getting a degree in artificial intelligence, although that is not actually the case. In traditional career tracks, skills, education, job roles and work experience need to be aligned to the greatest extent possible. In contrast, it is possible to realize Artificial Intelligence in several paradigms and potentially in all industries. 

AI skills are required in varying industry sectors, including healthcare, military, telecom, art, manufacturing, research, marketing, transportation and finance. There are alternative areas as well with massive opportunities embracing ethics, policymaking, civic planning and philosophy. So, there is a scope with AI for everyone, regardless of the industry they are willing to work in. 

Two of the most common approaches to making a career in AI is through formal education or online self learning with MOOC or PhD. Formal education is suited for those who wish to join academia or research and contribute towards developing artificial intelligence. For those who like building products or features, specialized and structured online courses are the ones to attend. Regardless of the form of learning, you need to recognize the areas of weakness and strength and then pursue a learning method that suits you the best. 

Honestly, the answer to this question can be quite subjective. It depends on the competence and talent of the student, the sources he/she is finding information from and the number of hours he/she puts in to execute and practice the theories learned. AI is a combination of technical skills and practical experience. You need to complete both these aspects in order to master the AI platform.

Now, for a beginner, learning AI may seem to be a very uphill battle. But don’t worry, with enough perseverance and patience you can easily master the subject and become a successful AI developer. There are several learning platforms and online tutorials specially designed for amateurs and entry-level developers who need to learn everything from scratch. This might take you anywhere between a year or two depending on how fast you can catch up

People who have some level of experience with AI and want to upgrade their skills can opt for the shorter online courses or read up books on AI. These books and courses brush over the fundamental concepts of AI and focus more on advanced principles and concepts. You even get to practice sheets and datasets to work on for an overall holistic learning experience. These programs continue for a duration of a few weeks or a couple of months at max.

Experienced AI developers looking for ways to brush up their skills and keep up with the changing trends of the industry can check out the advanced level courses that are but a few hours long. You have to put in about 10-20 hours of study, listen to a few lectures and practice on some datasets to get back in the game.

Artificial Intelligence is a vast field and you might face some difficulties getting started in the field. Here are some steps that will help you lean artificial intelligence:

1. Selecting a topic

Select a topic that interests you. It will keep you focused and motivated during the learning process. The mistake that most people make while learning AI is just reading everything that is available on the internet without implementing it. Such learning is not good for long term retention. What you need to do is focus on a specific problem and then try working on finding a solution for it.

2. Finding a Solution

Once you have focused on a problem, you need to start working on a solution covering the problem. What you need to do is find an algorithm for processing data into a form that a machine can understand. This data is then used for training a model, providing results, and evaluating the performance.

3. Improving the solution

Once you have found a solution that works, it's time to get creative. Start improving and evaluating the changes. This is done to make sure that the efforts and time you are spending on improvements are worth it or not.

4. Sharing

Write the solution and share it with fellow AI professionals to get feedback. You will be able to get advice from other experienced professionals. It is also valuable to get a second insight into your problem and solution.

5. Practicing

Now, you need to find solutions to different problems. Start with tabular data and then move on to images and unstructured data. It is very important that you know formulating machine learning problems properly. You must be familiar with turning the abstract objectives of the business into concrete problems fitting the specifics of AI.

6. Kaggle

Through Kaggle competitions you will be testing your skills by solving the problem that several AI professionals are working on. These competitions force you to try multiple approaches and then select the most effective one. Since you are joining a big community and communicating with other AI professionals on the forum, not only will you be learning collaboration but also how to share your learning and ideas with others.

Here is a list of AI technologies that you can use for taking your projects to a new level:

1. Tensor flow

This is an open-source Machine Learning framework that can be deployed across different platforms. This framework was created by Google to support its production objectives and research. Today, companies like eBay, Uber, Dropbox, Twitter, and Intel uses it. It is available in Python, Java, C++, Go, Haskell, Rust, and JavaScript. TensorFlow allows using flowgraphs for developing neural networks.

2. Keras

This is an open-source software library used for simplifying the process of creating deep learning models. Written in Python, this library can be deployed on other AI technologies like TensorFlow, Theano, Microsoft Cognitive Toolkit (CNTK). It is known for its modularity, ease of extensibility, and user-friendliness. It is responsible for running CPUs and GPUs optimally, supporting recurrent and convolutional networks, and fast prototyping.

3. Sci-kit learn

It is an open-source library created for machine learning. Written in python, it featured ML models like classification, clustering, dimensionality reduction, and regression. It’s primary focus is in data mining and analysis. Released in 2007, this library was designed on other open-source projects like NumPy, Matplotlib, and SciPy.

4. Microsoft Cognitive Toolkit

Previously termed as CNTK, Microsoft Cognitive Toolkit uses AI solution for empowering the Machine Learning projects to a whole new level. This is an open-source framework that trains deep learning algorithms so that they can start functioning like a human brain. Some of its features include providing efficient resource usage, interoperating with NumPy, integrating with Microsoft Azure easily, using highly-optimized components for handling data from C++, Python or BrainScript.

5. Theano

This is an open-source Python library that allows easy fashioning of different machine learning models. It is one of the oldest libraries that makes it an industry standard for developments in deep learning. The main aim of this library is simplifying the process of defining, assessing, and optimizing mathematical expressions. It takes your structures and converts them into efficient code which can be integrated with NumPy, native code and libraries like BLAS. Also, it is responsible for optimizing GPUs, providing code-testing capabilities and efficient symbolic differentiation.

You need to acquire the following skills for getting a job in the field of AI:

1.Programming

For getting a job in the field of AI, knowledge of a programming language is a must. There are several languages that you can go for. For example, Python is one of the most popular programming languages used in ML. R works great for plots and statistics. C++ can help speed your code up. Java can be used for working with Hadoop for implementing reducers and mappers in Java.

2.Statistics and Probability

This will help you get a clear understanding of algorithms like Naive Bayes, Hidden Markov Models, and Gaussian Mixture models. Only through in-depth knowledge of probability and statistics will you be able to work with these models. You need to learn how to use statistics as an evaluation metric: receiver-operator curves, confusion matrices, p-values, etc.

3.Algorithms and Applied Math

Once you have a clear understanding of how the algorithm works and the algorithm theory, you can start discriminating models like SVMs. Make sure that you make yourself familiar with subjects like quadratic programming, gradient descent, summations, partial differential equations, lagrange, and convex optimization.

4.Distributed Computing

When you are working in the field of AI, you will be dealing with large datasets on a daily basis. Processing of this cannot be done through a single machine. This requires distribution of the data through an entire cluster. Cloud services like Amazon’s EC2 and projects like Apache Hadoop make this distribution process cost-effective and easy.

5.Unix tools

There are several Unix tools designed for AI like cat, find, cut, tr, awk, grep, sort, sed, etc. Since, all the processing of data will be done on a Linux-based machine, you will require access to these tools. Make yourself familiar with its utility and functionality to make your life easier.

6.Advanced Signal Processing Techniques

One of the most important aspects of machine learning is feature extraction. You must have knowledge of different algorithms of advanced signal processing for finding different solutions to different types of problems. Some of these algorithms are wavelets, bandlets, shearlets, contourlets, and curvelets. You must also have a clear understanding of time-frequency analysis, convolution, and Fourier analysis.

7.Other skills

You must start reading up and keep yourself updated on technological innovations in the field of AI. read papers like Google Big Table, Google File System, Google Map-Reduce, and the Unreasonable Effectiveness of Data. There are also several learning books available online that you must go through.

AI is a huge field. Different studies have shown that AI research constitutes 10% of the total Computer Science research being conducted. Microsoft Academic Search can be used for getting the relevant figures on how many people study AI.

As per the Bureau of Labor Statistics, there are about 26,700 Computer and Information Science researched in the United States. There are more than 1000 members in the Special Interest Group on AI of ACM. There are more than 2000 members in the International Neural Network Society.

According to MAS lists, there are 1360 CS journals. Out of these 106 are in AI, 172 are in one of AI’s disciplines. From 2005 to 2010, 10% of all the publications listed under the CS heading were in the AI section and 20% were in one of the AI’s discipline’s section.

The number of AI researchers is increasing exponentially, thanks to the increased demand in AI and its related technologies. In OECD countries, the growth rate of science and engineering researchers was 3.3%. Information and Intelligent Systems’s budget from NSF has increased from 4% to 20%.

Every computer system or database has its own library that comes with preset functions and features. This allows users to work on different aspects, manage multiple tasks, and monitor their activities. So, why do we need AI? 

Well, Artificial intelligence does all of these things and more. Also, unlike the libraries, the AI database is constantly being upgraded and updated to keep up with the times. This means that the machine is learning and constantly improving itself to suit the needs of the customers. Also, AI offers better scope for interaction with the user. This means that you can actually ask the AI App questions that are not already fed into the system. The AI system is designed in a way that makes it possible for the machine to think and act as an autonomous entity.

The modern-day public library is also a far cry from the accessible and customizable databases that AI offers to its clients. Artificial intelligence platforms are also a lot quicker, a flexible and a lot more fun to work with. Your options are not limited to a few select questions and features. The machines are not equipped to deal with emergencies, take decisions on their own merit and do a lot of other things.

The future of AI looks even more promising, we can predict some interesting advancements in technology where researchers and AI engineers are figuring out ways to improve the user interaction experience. AI platforms are also said to be a more accurate options, offering tailor-made, personalized and intuitive in its approach.

Libraries have become a slightly obsolete option for developers these days as they have a lot of drawbacks and restrictions. AI, on the other hand, is a far more expansive and extensive alternative.

We have had a long relationship with machines, starting out but a century earlier with the advent of the industrial revolution. Back then, no one could have predicted all that man could achieve in such a short span of time. There was a time when computers as big as rooms were used to compute and calculate the simplest of calculations. And even that process took hours. As explained by the father of computers- Charles Babbage, computers worked on the GIGO principle where the machine only displays the data that was already fed or added to its database.

This made things a lot harder for people as the machine was not capable of thinking for itself. It only reproduced and recycled the data that it has. With the introduction of AI or artificial intelligence in the start of this century, things took a sharp turn for the IT sector. Suddenly there were machines that could interact with the user, was intuitive enough to understand its needs and customize the results in a way that best suited the situation.

With AI, developers found more flexibility and freedom as they could now depend on their systems to take care or share the load of the program. Combining the practicalities of machine learning and the insights of data science with the advanced AI tech, scientists were able to build high-tech, reliable platforms for business ventures, private holdings and other things. Today we have AI in almost every aspect of our lives. From Google assistant on our phones to Alexa in our homes- AI has made itself comfortable with our daily routine, becoming a part of everyday life with relative ease. AI started out as role playing platforms and chat bots, today you have AI apps, systems and AI integrated business holdings.

Artificial intelligence is no longer a concept that people would imagine as sci-fi movie plots. You can actually interact with machines, customize the system to your needs and get an in-depth understanding of the platform intuitively. This makes things a lot easier for both the end-user and the developers. It also makes the relationship between man and machine more flexible and interesting to explore. 

There are different types of AI platforms that are being explored, optimized and integrated into mainstream computers. Some of the most popular ones are as follows: 

Limited Memory

This is a type of AI that comes pre-programmed and has to be integrated into the system. The responses and actions of the machines are built into the system. This is but an advanced version of the computers you use in your daily life. These apps and platforms have a fixed memory and offer a role playing, interactive experience. Self-driving cars are a great example of this category of AI  

Reactions from Machines

The next type is the reactionary machines that respond to your queries with preset responses. These platforms have been around for quite some time now and have become a huge part of our lives. Everything from the GPS navigation system to chatbots etc. are examples of this type of AI.   

Cognitive Machines

Cognitive machines are AI platforms that have the ability to take simple decisions and act on them. The options and responses are fed, updated and optimized in the database and it is the discretion of the machine to produce results that best suit the situation.  

Machines that can Empathize 

Next, we have machines that are so realistic that they can feel human-like emotions and respond with empathy and sensitivity. The last stage of AI is self-awareness where the machine is aware of its existence as an entity that was created to help the user.  

Artificial Intelligence involves simulating intelligent behaviour in computers. The capabilities of AI are immense, but the technology is still relatively nascent. Yet, AI applications are already improving everyday life. Be it smart assistants like Amazon’s Alexa or Apple’s Siri, applications for improving customer service and the ability of utilizing big data insights for streamlining or enhancing operations, AI is rapidly becoming an important tool in modern business scenario and life in general. 

As per recent reports, the number of companies employing AI is expected to double over the coming year. Likewise, there has been a significant increase in the number of jobs that require AI skills. 

Artificially intelligent systems are designed to leverage clues from their environment for solving problems, assessing risks, making predictions and taking actions based on the input. Advances in AI has also resulted in the development of machine learning for making decisions or predictions without requiring explicit programming for performing the task. 

There are, however, certain ethical issues that come into play with the possibility of designing “intelligent” machines. As AI continues to grow in its utilization and importance, ethical issues like pre-existing biases for training AI, privacy invasions through facial recognition and social manipulation through news feed manipulation arise. These issues highlight the need to address the responsibility behind designing and adopting these technologies. 

The latest facial recognition applications are amazingly accurate and can even recognize faces in crowds. As such, it has the great capability of identifying criminals and finding missing persons. However, there are criticisms surrounding this technology regarding its ethics and legality. 

With more and more AI enabled devices being developed and used by enterprises and customers across the world, there is a need to keep these devices secure. The issue here is ensuring safety for a technology that is made to learn and modify its behaviour accordingly. It is not always possible for developers to determine why or how AI systems take certain actions. 

Below are the AI technologies that you can look forward to in 2019: 

1.Natural Language Generation  

It is a sub-discipline of AI used for transforming data into text. This allows the computers to communicate their ideas accurately. It is mainly used in computer service for generating market summaries and reports. Companies like Attivio, SAS, Cambridge Semantics, etc. offer this service. 

2.Speech Recognition  

More and more systems are developed every day that are transcribing human language and interacting with millions of customers through mobile apps and voice-response systems. Siri is one such example. Companies like NICE, OpenText, Nuance Communications, Verint Systems, etc, are offering voice recognition services. 

3.Virtual Agents 

This is a computer program that can interact with humans. Chatbots are an example of virtual agents. Currently, this service is used to provide support and as home managers. Companies like Apple, Amazon, Assist AI, IBM, Google, Microsoft, etc. are providing virtual agents. 

4.Machine Learning Platforms  

ML is a branch of AI and discipline of Computer science that is used for developing techniques enabling computers to learn. Algorithms, big data, development and training tools, APIs, and other machines are provided by the ML platforms. 

5.AI-optimized Hardware  

AI uses CPUs, new graphics, and processing designs for creating hardware that is user friendly. They are structured and designed specifically for executing tasks that are AI-oriented. For example, AI-optimized silicon chips that are inserted into portable devices. 

6.Decision Management  

There are some logic and rules introduced to the AI systems by Intelligent machines for setting up, training, maintaining, and tuning. There are several corporate applications that have incorporated decision management for assisting and executing an automated decision that makes their business more profitable. 

7.Deep Learning Platforms

It is a form of ML involving Artificial Neural circuits containing abstraction layers mimicking human brain. They can process data and create a pattern for decision making. Deep Instinct, Sentient Technologies, Peltarion, Fluid AI, Saffron Technology, MathWorks, and Ersatz labs are some of the companies providing deep learning options.

8.Biometrics

These are used for identifying, measuring, and analyzing physical aspects of the human body structure as well as human behaviour. With this, natural interactions between machines and humans through touch, speech, image, and body language recognition has become possible. 

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FAQs

The Course

Artificial intelligence is the technology of making our systems more intelligent and providing solutions to problems. AI is the hottest career in this digital age and AI experts certainly earn the big bucks. According to Neuvoo, the average salary for Artificial Intelligence related jobs is $73,552 per year or $38 per hour. This is around 2.5 times more than the average salary in America. This course will help you understand the core concepts of AI and use it to build intelligent solutions. You will also get in-depth prep help to clear interviews and land jobs.

On completing this course you will:

  • Get advanced knowledge on machine learning techniques
  • Learn about Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks
  • Gain knowledge about how artificial intelligence can be implemented in real-time
  • Be proficient with computer vision tool: OpenCV

By the end of this course, you will gain

  • Strong knowledge on Machine Learning Techniques
  • Ability to build a game playing agent
  • Learn to build real-time object detectors

Tools and Technologies used for this course are

  • OpenCV
  • Python
  • TensorFlow
  • Keras

There are no restrictions but participants would benefit if they have sound knowledge in Python and familiarity with Data Science.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are AI experts who have years of industry experience. 

Finance Related

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy  for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

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