Introduction to Artificial Intelligence (AI) Course

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.

To get an AI certification, you would need to complete a certification training by a credible training provider. By successfully passing the exam, you’ll obtain the certificate that affirms your knowledge of basic AI concepts, principles, usage, as well as associated risk and ethical requirements.

Introduction to Artificial Intelligence (AI) Course is a highly regarded and popular program on AI, perfect for individuals who are just starting out in their AI journey. It covers the core principles of AI and deep learning techniques with Keras and Tensorflow through 50 hours of instructor led training and hands-on exercises.

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.

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.