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
Group Discount

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.  

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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.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

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