OVERVIEW

74% of Indian business heads believe that AI can augment economic growth*. As modern organisations turn towards Machine Learning (ML) and Artificial 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 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.

*Source: Artificial intelligence in India – hype or reality, PricewaterhouseCoopers India, February, 2018

KEY BENEFITS

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

CERTIFICATE

Certificate Program in Machine Learning & AI with Python - Certificate Click to view certificate

    GET PROGRAMME INFO?

    • STARTS ON

      Coming Soon

    • DURATION

      6 Months
      (Live Online Sessions
      Every Sunday: 3:30 PM - 6:30 PM)

    • PROGRAM FEE

      INR 1,25,000 + GST

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

    Program Modules & Faculty

    syllabus

    • Basics of Python
    • 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
    • The Idea of training, testing, and validation
    • Cross-Validation
    • K-fold Cross-validation
    • Introduction to Linear Regression Analysis problem with examples
    • Solving Linear Regression using Matrix Inversion and Gradient Descent Based Approaches
    • The Idea of Regularisation
    • Lasso and ridge regression
    • Empirical Risk Minimisation principle
    • The Idea of Generalisation
    • Lazy and Active learners
    • K Nearest Neighbour Classification
    • Linear Discriminant Analysis
    • Bayesian Approaches for Classification
    • Naïve Bayes’ algorithm
    • Gaussian Discriminant Analysis
    • Parameter estimation using MLE, MAP
    • The Idea of EM algorithm for GMM
    • Tree based Classification
    • Decision Tree
    • ID3 algorithm for designing Decision Trees
    • Decision Tree for regression
    • Regularisation in Decision Tree
    • Random Forest
    • Support Vector machines
    • Margin Based Classification
    • SVM, linearly & nonlinearly separable cases
    • The 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
    • The Idea of different layers in CNN
    • CNN models for Image recognition (Alexnet, VGG, Resnet, Inception Net etc.)
    • Examples in Tensorflow/ Keras
    • Linear & Nonlinear programming
    • Gradient-based Optimisation
    • Convex Optimisation
    • The Idea of Data Clustering and density estimation
    • Clustering techniques (K-means, Fuzzy C Means, Mean-Shift, DBSCAN)
    • Implementation of K-means in Python
    • Language modelling
    • Machine Learning techniques in NLP
    • Supervised and Unsupervised feature selection
    • Principal Component Analysis (PCA)
    • Independent Component Analysis (ICA)
    • Implementation of PCA in Python
    • Introduction to RL & example 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: Prof. Vinay Namboodiri (IITK)


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

    PROGRAM DIRECTORS & FACULTY

    Prof. Biplab Banerjee (Programme Director)
    Prof. Biplab Banerjee (Programme Director)

    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.

    Prof. Kumar Appaiah (Programme Director)
    Prof. Kumar Appaiah (Programme Director)

    Assistant Professor,

    Electical 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.

    Prof. Preethi Jyothi (Programme Faculty)
    Prof. Preethi Jyothi (Programme Faculty)

    Assistant Professor,

    Computer Science & Engineering

    Prof. Preethi Jyothi has received a 2017 Google Faculty Research Award for her research on accented speech recognition. Before joining IIT Bombay in 2016, she was a Beckman Postdoctoral Fellow at the University of Illinois at Urbana-Champaign and received her PhD in Computer Science in 2013 from The Ohio State University on university fellowship. Her work on statistical learning methods for pronunciation models and probabilistic transcriptions received Best Student Paper Awards at Interspeech 2012 and ICASSP 2016, respectively.

    Program Fee

    Dates Length Location Tuition Fees
    Coming Soon 6 Months Live Online Sessions
    Every Sunday: 3:30 PM - 6:30 PM
    INR 1,25,000 + GST
    Deadline Application Fee
    Round 1 Nov 21, 2020 -
    Round 2 Dec 21, 2020 -
    Remarks Instalment Amount
    Instalment 1 Within 7 days of selection INR 62,500 + GST
    Instalment 2 7 days prior to the program start date INR 62,500 + GST

    Student Loan Details

    We currently do not offer Student Loans for this program. However, we are working on providing an option and will alert you as soon as it is made available.

     

    SYSTEM REQUIREMENTS

    SYSTEM REQUIREMENTS TO ATTEND A LIVE ONLINE CLASSES
    This programme includes live online classes. To attend a live online class you will need to have a PC/Laptop/Mac with

    • Speakers and microphone: built-in or a USB plug-in or wireless Bluetooth
    • Webcam: built-in or USB plug-in
    • Processor: with Dual Core 2Ghz or higher (i3/i5/i7 or AMD equivalent)
    • RAM: 4 GB or higher
    • OS: Either MacOS 10.7 or higher OR Windows 8 or higher
    • An internet connection: Minimum bandwidth of 3.0 Mbps (up/down)
    • Browser: IE 11+, Edge 12+, Firefox 27+, Chrome 30+
    • Zoom software client installed on your PC/Laptop/Mac

    We use the Zoom software application to conduct live online classes. Zoom works on a variety of PCs/Laptops/Mac systems and also on phones and tablets. You can join your live online class from a phone or tablet if it supports the Zoom client. We recommend that you attend classes from a PC/Laptop/Mac.