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Upskill in ML and AI with Python and become industry-ready!

CERTIFICATE PROGRAM IN MACHINE LEARNING & AI WITH PYTHON

Starts December 30, 2021 | Duration: 6 Months
DELIVERED VIA LIVE ONLINE SESSIONS

Why Machine Learning?

From apps to businesses, machine learning helps make sense of data

Why Artificial Intelligence?

To prepare for jobs of the future with Articial Intelligence

Why Python?

The #1 programming language that can perform extremely complex tasks
  • Why This Program?
  • 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 benificial 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 Overview

OVERVIEW The AI industry market size is projected to be $266.92 billion by 2027 at a CAGR of 33.2%*. As modern organisations turn towards Machine Learning ( ML) and Articial Intelligence (AI) for responsive and automated business solutions, skilled talent that will help them harness the full potential of these technologies, are in high demand. 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 up-skill 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 classication and regression, optimization techniques, neural networks, decision trees, agent-based models, and deep convolutional networks.

Learn from one of India's Best Engineering Schools

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 Completion from IIT Bombay

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

SKILLS YOU WILL LEARN

Python, NumPy and Pandas

Sort, study & earrange data, run analyses, build DataFrames from scratch

Scikit & SciPy

Construct predictive linear models for maximum precision

ML Algorithms

Understand mathematical & statistical models

NLP

Build text classication systems using linear classiers & deep learning

RL

Build agent-based models

Learning Outcomes

PROGRAM MODULES

This interactive live online program will be spread across six months, with classes held once a week. Participants will get hands-on learning during online lab sessions which will reinforce the concepts presented in the program modules. Assignments will be in Python using specialised tools such as Excel and Gambit, and participants will be given access to problem descriptions, base code, and relevant libraries.

Module 1 : Basics Of Python

All The Basics Of Python Will Be Thought & Brushed UP

Module 2: Mathematical Backgrounds

Statistics Probability Linear Algebra

Module 3: Python

Numerical Computing with Python (NumPy, Matplotlib) Introduction to Pandas for data import and export (Excel, CSV etc.) Basic Introduction to Scikit learn

Module 4: Introduction to Machine Learning

Introduction to Machine Learning with applications to dierent domains Supervised Learning Unsupervised Learning Semi-Supervised Learning Reinforcement Learning Idea of hypothesis space Machine learning as hypothesis selection problem Finite and Innite hypothesis space Complexity of the hypothesis space The Idea of training, testing, and validation Cross-Validation

Module 5: Regression Analysis

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-o Undertting and Overtting of models

Module 6: Classification - Introduction and KNN

Idea of empirical risk minimization principle Idea of generalization Lazy and active learners K nearest neighbor classification Linear discriminant analysis

Module 7: Classification: Probabilistic classifiers

Bayesian approaches for classication Naïve Bayes’ algorithm Gaussian Discriminant Analysis Parameter estimation using MLE, MAP, Idea of EM algorithm for GMM

Module 8: Classification: Decision Tree

Tree based classication, Decision Tree, ID3 algorithm for designing decision trees, Decision Tree for regression, Regularization in Decision Tree, Random Forest

Module 9: Classification: Support Vector Machines Networks

Support Vector machines Margin Based classication SVM and linearly and nonlinearly separable cases Idea of Kernels Multi-class SVM Examples using LIBSVM

Module 10: Classification: Neural Network and CNN

Introduction to Neural Networks, Biological neuron model and extension to articial neuron models McCulloch-Pitts model Multi-layer perceptron Back-propagation based training of neural networks Introduction to convolution networks Idea of dierent layers in CNN Discussions on dierent CNN models for image recognition (Alexnet, VGG, Resnet, Inception Net etc.) Examples in Tensorow / Keras

Module 11: Optimization in ML

Linear and nonlinear programming, Gradient based optimization, Convex optimization

Module 12: Unsupervised Learning

Idea of data clustering and density estimation K-means Fuzzy C Means Mean-Shift DBSCAN clustering techniques Implementation of K-means in Python

Module 13: NLP and text analysis

Language modelling, Machine Learning techniques in NLP

Module 14: Feature selection and Dimensionality Reduction

Supervised and Unsupervised feature selection PCA ICA Implementation of PCA in Python

Module 15: Reinforcement Learning

Introduction to RL, Example of RL models, Markov Decision Process, Policy and Value Iterations, Bellman Equation, Temporal Dierence Learning, Q Learning, Introduction to deep RL.

Tentative Guest Lectures

ML in Finance: Prof. Piyush Pandey (SOM) ML in computer vision: Vinay Namboodiri (IITK) ML in speech and text processing: Preethi Jyoti (CSE)

CASE STUDIES

Travel

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. Besides academic and sponsored research, he teaches graduate and undergraduate courses in structural engineering and probabilistic methods. He has conducted industrial research/consultancy works in the areas related Take this on the previous line, there is gap, structural reliability, design of steel and RC structures, and retrofitting of structures. He is a member of the Bureau of Indian Standards’ committee CED 7 on steel structures. For the academic year 2014-15, he was on sabbatical as a guest professor in the Chair of Structural Dynamics and Earthquake Engineering in IBK, D-BAUG, ETH Zurich.

Prof. Biplab Banerjee

Assistant Professor & PROGRAM DIRECTOR

A small river named Duden flows by their place and supplies it with the necessary

Prof. Palaniappan Balamurugan

PROGRAM FACULTY & Assistant 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. Further to his PhD, he had post-doctoral research experience at INRIA, Paris where he worked with Professor. Francis Bach, and at the Signal, Statistics and Machine learning (S2A) group at Telecom ParisTech, where he worked with Professors Stephan Clemancon and Florence d’Alche Buc. He has published across dierent domains like Neural Networks, Data Mining, Machine Learning, etc. and has conducted courses in Machine Learning, Deep Learning, Linear Systems and Applications of Stochastic Models. Prof. Balamurugan has also won numberous awards from IISc, IBM, Infosys India and IEEE ICDM.

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.

PAST PARTICIPANT PROFILE

INDUSTRIES

FUNCTIONS

ABOUT IIT BOMBAY

Established in 1958, the second of its kind, IIT Bombay was the rst to be set up with foreign assistance and
has emerged as one of the top technical universities in the world. The institute is recognised worldwide as
a leader in the eld of engineering education and research. Reputed for the outstanding calibre of students
graduating from its undergraduate and postgraduate programs, the institute attracts the best students
from the country for its bachelor’s, masters and doctoral programs. Research and academic programs at IIT
Bombay are driven by an outstanding faculty, many of whom are reputed for their research contributions
internationally. IIT Bombay also builds links with peer universities and institutes, both at the national and
the international levels, to enhance research and enrich its educational programs. The alumni have
distinguished themselves through their achievements in and contributions to industry, academics,
research, business, government and social domains. Over the years, the institute has created a niche for its
innovative short-term courses through continuing education and distance education programs. Members
of the faculty of the institute have won many prestigious awards and recognitions, including the Shanti
Swaroop Bhatnagar and Padma awards.