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STARTS ON TBD Live Online
DURATION 6 Months Live Online Sessions 3 hours/ week Saturday 3:30 PM - 6:30 PM
PROGRAM FEE INR 1,25,000 + GST View Payment Plan Special Corporate Enrolment Pricing Documents Required to Apply
STARTS ON TBD Live Online
DURATION 6 Months Live Online Sessions 3 hours/ week Saturday 3:30 PM - 6:30 PM
PROGRAM FEE INR 1,25,000 + GST View Payment Plan Documents Required to Apply
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Early Bird Registration Benefit

INR 12,500 + GST

We are offering an early bird registration benefit on the program fee exclusively for participants who apply by TBD.
The final Early Bird fee will be INR 1,12,500 + GST.

Program Overview

Keeping this need in mind, the Indian Institute of Technology Bombay (IIT Bombay) has designed the Certificate Program in Machine Learning & AI with Python to upskill and train professionals in the world’s most in-demand programming language.

This six-month program, delivered through live online sessions by leading IIT Bombay faculty and industry experts, will enable participants to leverage ML and AI for automation, better decision-making, and competitive advantage. Participants will also get a comprehensive understanding of key machine learning algorithms, including popular methods like classification and regression, optimisation techniques, neural networks, decision trees, agent-based models, and deep convolutional networks.

Eligibility:
Any Graduate/ Diploma holder with minimum one-year work experience. Working knowledge/ experience of programming languages like Java or C++. Knowledge of linear algebra, calculus and statistics is desired

  • 77%

    Of the total devices that we presently use are utilizing ML

    (Source: Analytics Insight, 2020)

  • $30.6 B

    Predicted growth of the global machine learning market by 2024.

    (Source: Business World IT, 2021)

  • $266.92 B

    Expected market size for Artificial Intelligence by 2027.

    (Source-Analytics Insight, 2021)

Who is this Program for?

The Certificate Program in Machine Learning & AI with Python is ideal for every professional, who understands linear algebra, calculus, and statistics, and is a graduate with minimum one-year work experience

Joining this program will be beneficial if you:

  • Oversee software development, machine learning projects and/ or manage teams of software developers
  • Want to work or lead machine learning/ AI engineering projects
  • Possess a working knowledge/ experience of programming languages like Java or C++

Program Highlights

Experience interactive live online learning through live lectures and real-world case studies

Interact with leading IIT Bombay faculty and industry experts

Participate in peer-to-peer learning and networking

Receive a Certificate of Participation from IIT Bombay

Build a solid foundation with the principles of ML and AI with Python

Succeed with Eruditus Career Services workshops and job placement assistance

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Learning Outcomes

  • Conduct mathematical operations on a wide range of data using NumPy
  • Operate Pandas to sort through and rearrange data, run analyses, and build DataFrames from the outset
  • Gain quicker and relevant insights by visualising data with Matplotlib
  • Use Scikit to construct predictive linear models to forecast outcomes with maximum precision
  • Build predictive models using neural networks and decision trees
  • Build text classification systems with NLP using both linear classifiers and deep learning methods
  • Differentiate between optimisation techniques and how they solve learning problems across models, minimise errors or maximise rewards
  • Build AI models using agent-based models that run search algorithms and achieve their tasks
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Program Modules

  • Statistics
  • Probability
  • Linear Algebra
  • Numerical Computing with Python (NumPy, Matplotlib)
  • Introduction to Pandas for data import and export (Excel, CSV etc.)
  • Basic Introduction to Scikit learn
  • Introduction to Machine Learning with applications to different domains
  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Reinforcement Learning
  • Idea of hypothesis space
  • Machine learning as hypothesis selection problem
  • Finite and Infinite hypothesis space
  • Complexity of the hypothesis space
  • The Idea of training, testing, and validation
  • Cross-Validation
  • Introduction to the Linear Regression Analysis problem with examples
  • Solving Linear Regression using Matrix Inversion and Gradient Descent Based Approaches
  • The Idea of Regularisation
  • Lasso, ridge, and elastic net regularization
  • Bias-Variance trade-off
  • Underfitting and Overfitting of models
  • Idea of empirical risk minimization principle
  • Idea of generalization
  • Lazy and active learners
  • K nearest neighbor classification
  • Linear discriminant analysis
  • Bayesian approaches for classication
  • Naïve Bayes’ algorithm
  • Gaussian Discriminant Analysis
  • Parameter estimation using MLE, MAP, Idea of EM algorithm for GMM
  • Tree based classification,
  • Decision Tree,
  • ID3 algorithm for designing decision trees,
  • Decision Tree for regression, Regularization in Decision Tree, Random Forest
  • Support Vector machines
  • Margin Based classification
  • SVM and linearly and nonlinearly separable cases
  • Idea of Kernels
  • Multi-class SVM
  • Examples using LIBSVM
  • Introduction to Neural Networks,
  • Biological neuron model and extension to artificial neuron models
  • McCulloch-Pitts model
  • Multi-layer perceptron
  • Back-propagation based training of neural networks
  • Introduction to convolution networks
  • Idea of different layers in CNN
  • Discussions on different CNN models for image recognition (Alexnet, VGG, Resnet, Inception Net etc.)
  • Examples in Tensorflow / Keras
  • Linear and nonlinear programming,
  • Gradient based optimization,
  • Convex optimization
  • Idea of data clustering and density estimation
  • K-means
  • Fuzzy C Means
  • Mean-Shift
  • DBSCAN clustering techniques
  • Implementation of K-means in Python
  • Language modelling,
  • Machine Learning techniques in NLP
  • Supervised and Unsupervised feature selection
  • PCA
  • ICA
  • Implementation of PCA in Python
  • Introduction to RL,
  • Example of RL models,
  • Markov Decision Process,
  • Policy and Value Iterations,
  • Bellman Equation,
  • Temporal Difference Learning,
  • Q Learning,
  • Introduction to deep RL
  • ML in Finance: Prof. Piyush Pandey (SOM)
  • ML in computer vision: Vinay Namboodiri (IITK)
  • ML in speech and text processing: Preethi Jyoti (CSE)

Note:
- Modules/ topics are indicative only, and the suggested time and sequence may be dropped/ modified/ adapted to fit the total program hours.

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CEP CHAIR

Prof. Siddhartha Ghosh

Prof-in-Charge, CE & QIP

He is currently a Professor in the Department of Civil Engineering, IIT Bombay. After completing his Ph.D. from the University of Michigan in 2003, he joined IIT Bombay, where he has held various academic positions. His research interests are primarily in the application of probabilistic methodologies in earthquake risk reduction. He has supervised/is supervising several doctoral and masters research students working in the areas of performance-based seismic design, structural reliability, design of structural and cold-formed steel, analysis of masonry domes and arches, etc.

Programme Director

Prof. Biplab Banerjee

Assistant Professor,
Machine Learning and Visual Computing

Prof. Biplab Banerjee is currently working as an Assistant Professor at the Center of Studies in Resources Engineering (CSRE) and is an associated faculty at the Center for Machine Intelligence & Data Science (C-MInDS) at IIT Bombay. His research areas include advanced machine learning and deep learning with applications to computer vision. He has a Masters in Computer Science & Engineering from Jadavpur University (2010) and a PhD in Machine Learning from IIT Bombay (2015), respectively. He subsequently worked as a post-doctoral researcher at the University of Caen, France and the Istituto Italiano di Tecnologica in Italy. He is currently leading the Deep Learning in Computer Vision Research Group, consisting of several graduate and undergraduate students.

Program Faculty

Prof. Palaniappan Balamurugan

Assistan Professor,
Industrial Engineering and Operations Research

Prof. P Balamurugan is an Assistant Professor in the Industrial Engineering and Operations Research department at IIT Bombay. He works in the areas of Machine Learning, Deep Learning and Data Mining. He completed his Masters in Computer Science Engineering and his PhD, both from Computer Science and Automation Department, IISc, Bengaluru.

Prof. Kumar Appaiah

Assistant Professor,
Electrical Engineering

Prof. Kumar Appaiah is an Assistant Professor in the Department of Electrical Engineering. Before joining IIT Bombay, he was working in Qualcomm Research, New Jersey on problems related to visible light communication. He has over 15 years of experience with programming in Python and has built several numerical computing, web and general-purpose applications using Python and other programming languages. Prof. Kumar Appaiah is B.Tech and M.Tech from IIT Madras and has a PhD from the University of Texas at Austin

Note:
- This is a tentative list, and the confirmed faculty will be shared closer to the program commencement.

Program Certificate

Participants with requisite attendance of 80% will receive a certificate of completion from IIT Bombay - CEP department.

Sample Certificate

Note: All certificate images are for illustrative purposes only and may be subject to change at the discretion of IIT Bombay

Past Participant Profiles

Work Experience
Past Participant Experience
Top Industries
  • IT & Services
  • Banking & Finance
  • Manufacturing
  • Consulting
  • Retail
  • Healthcare
Top Functions
  • Consulting
  • Engineering
  • Operations
  • Finance / Accounting
  • General Management
  • Human Resource
Top Companies
  • Bank of America Continuum India Pvt Ltd
  • Deutsche Bank
  • Fedex
  • Siemens
  • Oracle India
  • Zoom

Early applications encouraged. Limited seats are available.

View Payment Plan
Special Corporate Enrolment Pricing

Round 1: The first application deadline is
TBD.

Round 2: The second application deadline is
TBD.

Round 3: The third application deadline is
TBD.

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In collaboration with Eruditus
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