AI and Deep Learning with TensorFlow

Master Deep Learning - the mean to culminate Machine Learning into Artificial Intelligence. Deep Learning is the technique to implement Machine Learning and eventually achieve practical implementations of artificial intelligence. Learn Machine Learning paradigms, Deep Learning, Multi-task Learning, Convolution Networks, Neural Networks, Multidigit Number Recognition, autoencoders, Boltzmann Machines and practical implementation of Deep Learning.

  • 24 Hours of Live Instructor Led Sessions
  • Hands-on assignments
  • Case Study Based Project
  • Verified certificate from NIIT
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Online Instructor-Led

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Course Features

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AI & Deep Learning with TensorFlow

AI and Deep Learning with Tensorflow program covers various Machine Learning Paradigms, how Deep Learning functions as a tools to achieve AI goals starting from Machine Learning. This course empowers you to master concepts of Neural Network Architecture, Regularisation for Deep Learning and Convolution Networks. Learner will also develop the understanding of Computational Graphs unfolding, various types of neural networks in terms of Deep Learning, Boltzmann Machines and Deep Belief Networks.

Finally, learner will master the practical implementation of building and training neural networks, interpret and unfold patterns and correlations using Open Source Software Library - Tensorflow by Google.

Course Objectives

By the end of the course, learner will be able to:

  • Understand various Machine Learning Paradigms
  • Understand Supervised & Unsupervised Learning Algorithms
  • Master basic concepts of Deep Learning
  • Learn practical aspects of Recurrent and Recursive Neural Networks
  • Understand large scale Deep Learning
  • Implement the learnt concept in a case study based Project
Module 1:

Machine Learning Basics

Module 2:

Introduction to Neural Nets and Deep Learning

Module 3:

Introduction to TensorFlow

Module 4:

Convolutional Networks

Module 5:

Recurrent and Recursive Nets

Module 6:

Unsupervised Learning: Autoencoders, RBM

Module 7:

Practical Methodology

Module 8:

Hands on Project


All our instructors have industry experience and extensive functional expertise in the fields they train. They are handpicked and trained to deliver a great online learning experience.

  • The assessment will be done on the basis of an online test and Project Evaluation at the end of the course.
  • Weightage of Project Evaluation is 30% and that of Online test is 70%. Minimum pass percentage for online test is 70.
  • On completion of the Assessment (Project + Test) with a minimum of 70% marks, will issue a certificate of successful completion from NIIT.
  • Five re-attempts will be provided to clear online test in case the candidate scores less than 70%.
  • A Participation certificate will be issued if the candidate does not score 70% in the Assessment.
  • Soft copy of the certification will be issued to the participants, on completion of the course.

Learner should have knowledge of a computer programming language preferably R or Python, Statistics & Probability and basic concepts of Machine Learning


What is the Case Study based Project which I need to develop as part of this program?

Project Title: Identify Numeric data in an Image of Numbers

Project Brief: Define a Neural Network model to be incrementally trained to recognize digits from a set of images of single digits. The model would be trained gradually into recognizing digits with the first batch of images and later it would identify digits from another set of such images to accurately identify the digits.

Who should join this course?

AI, Machine Learning and Deep Learning fall under one umbrella - Data Science. This course is most suited for:

Object Oriented application developers who aspire to carve out their career in the field of Data Science.

Professionals having basic understanding of Data Science concepts. Statistics and Probability professionals involved in Data Mining and Interpretation techniques. Aspiring engineers who need to keep abreast with the advancing paradigm shift from feature engineering.

Software professionals who already have Big Data background and aspire to delve into developing automated learning features of a system.

Why should I join this course?

For Computer Science engineers and Computer Science Professionals, Deep Learning, also known as Artificial Neural Networks is a game changer technique serving as a sharp turning point for their career. Deep Learning has revolutionised the way scientific computing is applied.

It enables CS Engineers to stay in their career by mastering the huge paradignm shift the way Machine Learning is now being implemented to achieve Artificial Intelligence.

AI is not future. It is rather near future and to keep up with the changing demand of the industry one ought to delve into Deep Learning.

What happens if I miss a session?

All the live sessions are recorded and available for later view. Learners can refer to recordings of a missed session at their convenience.

Where can I find my session schedule?

The session schedule will be available in the Student portal - Learning Plan section. You can login to your account to view the same.

Do you provide any study materials?

The study material will be available in the Student portal - Resources section. You can login to your account to view the same.

What is your refund policy?

Upon registering for the course, if for some reason you are unable or unwilling to participate in the course further, you can apply for a refund. You can initiate the refund any time before start of the second session of the course by sending an email to , with your enrolment details and bank account details (where you want the amount to be transferred). Once you initiate a refund request, you will receive the amount within 21 days after confirmation and verification by our team. This is provided if you have not downloaded any courseware after registration.

What are the minimum system requirements to attend the program?

    Minimum system requirements for accessing the courses are:

  • Personal computer or Laptop with web camera
  • Headphone with Mic
  • Broadband connection with minimum bandwidth of 1 Mbps . However, recommend is 2 Mbps.
  • A self-diagnostic test to meet necessary requirements to be done is available at

    Please note that webcam, mike and internet speed cannot be verified through this link.

Is there an official support desk for technical guidance during the training program?

Yes. For immediate technical support during the live online classroom sessions, you can call 91-9717992809 or 0124-4917203 between 9:00 AM and 8:00 PM IST. You can write to for all other queries and our team will be happy to help you.

Course Features

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