Artificial Intelligence

Course Features

Course Details

Mastering Artificial Intelligence

Accelerate your career with Artificial Intelligence . Enrol Today!

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This course will starts with broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems and will take you to the expert level.

You will learn about Python Programming, Machine Learning and Artificial Intelligence as part this course.

Hands on experience will be gained and the Artificial Intelligence training is delivered by experienced AI Developers.

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Training Features

Instructor-led Sessions

48 Hours of Online Live Instructor-Led Classes. Weekend Class : 16 sessions of 3 hours each. Weekday Class : 24 sessions of 2 hours each.

Lifetime Access

You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various Artificial Intelligence concepts.

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.


Live project based on any of the selected use cases, involving implementation of the various Artificial Intelligence concepts.


Towards the end of the course, you will be given access to online Test. myTectra certifies you as an Artificial Intelligence Expert based on the scoring of 60% or above.

Course Outline

Chapter 1:Introduction

  • In this course you will learn to implement mathematical ideas in machine learning. You will investigate the process of learning and understand the application of various learning algorithms.
  • Prerequisites: The courses assignments and notes will use python programming language and expects a basic knowledge of python. We assume the student has completed the Machine Learning Foundations or has an equivalent fluency in mathematics and fundamentals.

Chapter 2: Linear Models

  • Understand linear approximation and modelling of problems and develop linear models

Chapter 3: Dimensionality Reduction

  • Use ideas from linear algebra to transform dimensions and warp space providing additional flexibility and functionality to linear models.

Chapter 4:SVM

  • Develop and implement kernel based methods to develop nonlinear models to solve few complex tasks.

Chapter 5:Nearest Neighbours, K-means, and Gaussian Mixture Models

  • Review pattern recognition ideas with distance and cluster based models to understand similarity measures and grouping criteria.

Chapter 6:Naive Bayes and Decision Trees

  • Dive into applications of bayes theorem and the use of decision criteria when learning from data.

CHAPTER 7:Search

  • Look at search from the perspective of graphs, trees and heuristic based optimizations.

CHAPTER 8:Logic and Planning

  • Discover ways to encode logic and develop agents that plan actions in an environment.

CHAPTER 9:Reinforcement Learning and Hidden Markov Models

  • Engineering agents that learn from a sequence of actions using rewards and penalties.

CHAPTER 10:Q-Learning and Policy gradient

  • Operate in a stateful world over value and policy approximations tasks

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More About Artificial Intelligence

What is Artificial Intelligence?

Artificial Intelligence (AI) is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem

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Is Artificial Intelligence Possible?

``Artificial Intelligence has been brain-dead since the 1970s.`` This rather ostentatious remark made by Marvin Minsky co-founder of the world-famous MIT AI

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Why AI


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