A Comprehensive Deep Learning Program Taught by IIT Bombay Faculty

Deep Learning Certificate Program

  • Online learning
  • 3 months
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  • learning partner AWS educate

India's Most Comprehensive Deep Learning Program

Deep Learning Certificate Course is a 3-month structured online course which covers the foundation of deep learning, Neural Network, Computer Vision, NLP and more.

Great Learning Hours

80+ hours of learning

great-learning-hands-on-project

Hands-on projects

Great Learning Live Support

Live support sessions

Great Learning Certificate

Great Lakes certificate

Great Learning gpu-lab

GPU accelerated cloud lab

Great Learning industry session

Industry sessions

Learn From IIT Bombay & Great Lakes Faculty

Dr. Arjun Jain

Adjunct Faculty Member, Department of Computational and Data Sciences, IISc

Deep Learning and Computer Vision

Dr. Jain is the co-founder of Perceptive Code, a Silicon Valley AI startup that builds intelligence into automobiles using Deep Learning. He is part of the Theano development team and a contributor to Torch - both of which are widely used libraries for Deep Learning.

Dr. Jain received his PhD from the Max Planck Institute in Germany and his post-doc from NYU where he worked with Yann LeCun.

Dr. Amit Sethi

Faculty, IIT Bombay

Image Processing and Machine Learning

Prof. Sethi is currently a faculty member at IIT Bombay where his research is focused on applying deep learning methodologies to digital pathology for analysis of cancer tissues. He was previously a faculty member at IIT Guwahati and spent many years at ZS Associates, a leading management consulting firm, at their offices in Chicago.

Prof. Sethi holds MS and PhD degrees from University of Illinois at Urbana-Champaign and BTech from IIT Delhi.

Prof. Mukesh Rao

Faculty, Great Learning

Machine Learning

Prof. Mukesh Rao has almost 3 decades of experience in the Analytics and Machine Learning industry. He has designed and implemented Machine Learning algorithms for abuse detection, social media analysis and report generation using MapReduce. Prior to this, Prof. Rao was with Wipro for over 12 years where he was the Head of PM Academy.

Learn From IIT Bombay & Great Lakes Faculty

Dr. Arjun Jain

Adjunct Faculty Member, Department of Computational and Data Sciences, IISc

Deep Learning and Computer Vision

Dr. Jain is the co-founder of Perceptive Code, a Silicon Valley AI startup that builds intelligence into automobiles using Deep Learning. He is part of the Theano development team and a contributor to Torch - both of which are widely used libraries for Deep Learning.

Dr. Jain received his PhD from the Max Planck Institute in Germany and his post-doc from NYU where he worked with Yann LeCun.

Dr. Amit Sethi

Faculty, IIT Bombay

Image Processing and Machine Learning

Prof. Sethi is currently a faculty member at IIT Bombay where his research is focused on applying deep learning methodologies to digital pathology for analysis of cancer tissues. He was previously a faculty member at IIT Guwahati and spent many years at ZS Associates, a leading management consulting firm, at their offices in Chicago.

Prof. Sethi holds MS and PhD degrees from University of Illinois at Urbana-Champaign and BTech from IIT Delhi.

Prof. Mukesh Rao

Faculty, Great Learning

Machine Learning

Prof. Mukesh Rao has almost 3 decades of experience in the Analytics and Machine Learning industry. He has designed and implemented Machine Learning algorithms for abuse detection, social media analysis and report generation using MapReduce. Prior to this, Prof. Rao was with Wipro for over 12 years where he was the Head of PM Academy.

The Deep Learning Course Learning Experience

Great Learning - Deep Learning certificate program by Arjun Jain
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World class faculty

Our faculty has been integral to the development and widespread adoption of Deep Learning. They have taught at the best institutions in the world and have worked with the likes of Yann LeCun, a founding father of Convolutional Networks. Our faculty has also spent years honing their craft in the financial, automotive and healthcare industries.

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Guided hands-on projects

Build confidence in your ability to build functional models through a series of industry-relevant deep learning projects. These hands-on projects, with guidance from the program’s learning support team, will give you an opportunity to learn by doing. Through this course, you will build an ePortfolio to showcase your expertise to the world.

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Structured learning

The Deep Learning Course takes a step-by-step approach to learning as we walk you through all the elements of neural networks, deep learning and their application to computer vision and natural language processing. Every week, the learning content will be augmented by learning support sessions that will answer your questions and guide you through projects.

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Labs with GPU access

Work on accelerated computing platforms with GPUs powered by AWS Educate and others that you will need to build and test deep learning models on complex data sets. You can access this lab from anywhere and won’t need to struggle with lack of computing power and incompatibility issues. Every student will receive $75 credits to work on AWS powered GPU labs in addition to access to free resources available.

India's Most Comprehensive Deep Learning Course

IIT Bombay Faculty | GPU Accelerate AI Lab

Program Structure

A structured 3-month online course with hands-on projects and learning support sessions on weekends

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Online content

Best-in-class content from expert faculty and industry sessions

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Hands-on projects

Solve real-world problems using widely used deep learning techniques

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Weekly learning support sessions

Live support sessions to guide your learning journey

Great Lakes Certificate & ePortfolio

Earn a Great Lakes certificate and create an ePortfolio to showcase your learning & projects in a snapshot. Your certificate and ePortfolio can also be shared on social media channels to establish your credibility in Deep Learning.

Great Learning - Deep Learning Certificate
Great Learning deep learning academic eportfolio

Curriculum

  • Python for Data Science
  • Linear Algebra - Vectors, Matrices, Tensors
  • Optimization techniques like Gradient Descent
  • Necessary statistics, probability and differential calculus
  • Neural Networks
  • Building blocks - feedforward, backpropagation, activation functions, hyperparameters, gradient descent, softmax, cross entropy loss
  • Deep neural networks
  • Implementing deep neural networks - learning rate, hyperparameter selection, weight initialization
  • Convolutional Neural Networks (CNN)
  • Image processing using CNN
  • Pre-processing, semantic segmentation, localization and detection
  • CNN architectures and Transfer Learning
  • Recurrent Neural Networks (RNN)
  • Long-short term memory (LSTM)
  • Common NLP techniques - Bag of words, POS tagging, tokenization, stop words
  • Word embedding - word2vec, GloVe
  • Sentiment analysis, Machine translation
Participants who complete the AWS specialization will get
  • done Additional Online Badges from AWS Educate including adding Alexa skills, upon successful completion of the learning pathways
  • done Access to AWS Educate Job Board for relevant Job Opportunities around the world
learning partner AWS educate
  • Official AWS Educate Learning Path - Machine Learning Scientist
  • Introduction to voice technology with Alexa badge
  • Introduction to Reinforcement Learning with AWS DeepLens
Languages and tools
  • Python
  • Python libraries such as Pandas, Numpy, Scipy, Scikit-learn
  • Visualization libraries such as Matplotlib
  • Natural language processing libraries such as NLTK
  • Deep learning packages such as TensorFlow and Keras
  • Github repositories

Projects

You'll work on three projects spread across Neural networks, Computer vision and Natural language processing. You will work on these projects using our GPU-enabled lab that will help you learn and solve problems without buying or installing the computing infrastructure yourself.

Computer Vision

Face Recognition

Natural Language Processing

Fake News Detection

Neural Networks

Recognizing numbers or text in photographs

Learning Outcomes

  • done Learn to use Python, Tensorflow and Keras to develop deep learning applications.
  • done Learn deep learning methodologies to process not only image based datasets but also raw text, numbers etc.
  • done Develop ability to independently solve business problems using deep learning techniques.
  • done Develop a verified portfolio with hands on deep learning projects that will showcase the new skills acquired to employers.
  • done Become proficient with AWS for Deep Learning.
  • done Ability to build, train, and deploy Deep Learning models in Cloud using AWS.
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Admission Details

Eligibility

  • done We recommend applicants to have at least 2 years of programming experience.
  • done Familiarity with statistics, algebra, probability and exposure to data analysis is preferred.
Grear Learning admission and selection process

Selection Process

Step 1

Fill application form

Step 2

Application review

Post application you will need to go through a screening call with the Admission Director's office.

Step 3

Final Selection

Your profile along with the review from the screening call will be shared with the Program Director for final selection.

Fee Details

Great Learning rupee icon 75,000 + GST

Fee Includes

80+ hours of learning

Live support and industry sessions

GPU lab for projects

Payments

Candidates can pay the course fee through

account_balance
Net Banking
credit_card
Credit/Debit Cards
Cheque, DD

Financial Aid

Candidates can pay the course fee in 2 equal parts. We also have tie up with various banks that provide an EMI option to candidates.

Upcoming Application Deadline

Today

We follow a rolling admission process and admissions are closed once the requisite number of participants enroll for the upcoming batch. So, we encourage you to apply early and secure your seat.

Apply Now

Frequently Asked Questions

The Deep Learning certificate is awarded by Great Lakes Executive Learning. Please note that certificate for Deep Learning Certificate Program is not issued by IIT Bombay.
Learning support sessions are interactive Q&A sessions conducted live during the course of the program for students to clarify their doubts, and receive guidance on how to advance their project work.
We recommend the candidates to have at least 2 years of professional experience in a technical role and have basic knowledge of probability, statistics and programming language. Having said that, we also offer introductory courses on Statistics and python to familiarise the students with.
The Deep Learning Course is taught by prominent IIT Bombay and Great Lakes faculties and experienced analytics professionals. Access the faculty section for further details.
The program includes various hands-on Real-world projects. You will analyse problems using a range of tools & techniques that you have learned in the class. We also offer learning support sessions to help students with their queries on projects.
We provide an accelerated computing lab with GPUs and all the relevant tools that you will need to complete your projects. You can access this lab online from anywhere.
No. The content is not downloadable. However, we believe that learning should be continuous and hence, all the learning material in terms of lectures and reading content would be available to you on the LMS even after the completion of the course.
The Deep Learning Course is a holistic and rigorous program and follows a continuous evaluation scheme. Quizzes and experiential learning projects help us evaluate a candidate's understanding of the concepts learned.
Candidates do have a provision to pay the fees in 2 equal installments. Further we also have tie-ups with leading banks who provide EMIs for the program. Please contact the admissions helpline at +91 99166 00701 for more details.
The Deep Learning course is designed to engage participants every week. All participants are required to learn from videos every week. However, attending leaning support sessions is optional, but recommended. You need to attend it only if you need to clarify your doubts regarding projects.
All projects are mandatory and evaluated. If you miss a project, you will not be eligible to earn the certificate.
Python, Keras, Tensorflow, Natural Language Toolkit, Github