PG Diploma in

Machine Learning and AI

Online11 MonthsRs. 2,75,000 (Incl. Taxes)

Complete a Rigorous Post-Graduate Program

Upon successful completion of the program, you will be awarded a Post Graduate Diploma Degree from IIIT-Bangalore.

Learn. Experience. Master.

Cutting Edge Curriculum

Master advanced machine learning and artificial intelligence concepts such as Neural Networks, Natural Language Processing, Graphical Models and Reinforcement Learning.

For the Industry By the Industry

Learn cutting-edge applications through projects created in collaboration with the industry: Chat Bots, Image Classifiers and more.

Career Support

Apply for suitable Data Science, Machine Learning and AI profiles through our career support. You will get 1:1 industry mentorship which will help you prepare for the roles of tomorrow.

Program Vitals

Course Duration

Jan'18 - Dec'18Online, 11 months

Time Commitment

10-12 hoursper week

Program Fee

Rs. 2,75,000 (Incl. taxes) Flexible EMI Options available

Learn from the best Machine Learning & Artificial Intelligence Leaders

This program has been designed in collaboration with some of the most influential analytics leaders and top academicians in Machine Learning & AI.

S. Anand, CEO,

S. Anand


Prof. S. Sadagopan, Director, IIIT Bangalore

Prof. S. Sadagopan


Ph.D. (Purdue University)

Ujjyaini Mitra, Head of Analytics, Viacom 18

Ujjyaini Mitra

Head of Analytics

Dr. Chandrashekar Ramanatha, Associate Dean (Academics), IIIT Bangalore

R. Chandrashekar

Dean (Academics)

Ph.D. (Mississippi State University)

Hindol Basu, Partner, Tata iQ

Hindol Basu


Tata iQ

Srinath Srinivasa, Associate Dean (Academics), IIIT Bangalore

Srinath Srinivasa

Professor and Dean (R&D)

Ph.D. (Brandenburg Technical University)

Kalpana Subbaramappa, Ex - AVP, Decision Sciences, GENPACT

Kalpana Subbaramappa

Ex - AVP, Decision Sciences

Tricha Anjali, Associate Professor, IIIT Bangalore

Tricha Anjali

Associate Professor

Ph.D. (Georgia Tech)

Sai Alluri

Sai Alluri

PRO Analytics & Strategy Manager - Uber India

G Srinivasaraghavan, Professor, IIIT Bangalore

G Srinivasaraghavan


Ph.D. (IIT Kanpur)

Anshuman Gupta, PhD, Head - Data Science Program, Cognizant Technology Solutions

Anshuman Gupta, PhD

Director - Data Science

Dinesh Babu Jayagopi, Assistant Professor, IIIT Bangalore

Dinesh Babu Jayagopi

Assistant Professor

Ph.D. (EPFL)

Ankit Jain, Data Scientist, Uber

Ankit Jain

Data Scientist - Uber


Program Syllabus

  • Python for Data Analysis: Get acquainted with Data Structures, Object Oriented Programming, Data Manipulation and Data Visualization in Python
  • Introduction to SQL: Learn SQL for querying information from databases
  • Math for Data Analysis: Brush up your knowledge of Linear Algebra, Matrices, Eigen Vectors and their application for Data Analysis
  • Inferential Statistics: Learn Probability Distribution Functions, Random Variables, Sampling Methods, Central Limit Theorem and more to draw inferences
  • Hypothesis Testing: Understand how to formulate and test hypotheses to solve business problems
  • Exploratory Data Analysis: Learn how to summarize data sets and derive initial insights
  • Linear Regression: Learn to implement linear regression and predict continuous data values
  • Supervised Learning: Understand and implement algorithms like Naive Bayes and Logistic Regression
  • Unsupervised Learning: Learn how to create segments based on similarities using K-Means and Hierarchical clustering
  • Support Vector Machines: Learn how to classify data points using support vectors
  • Decision Trees: Tree-based model that is simple and easy to use. Learn the fundamentals on how to implement them
  • Basics of text processing: Get started with the Natural language toolkit, learn the basics of text processing in python
  • Lexical processing: Learn how to extract features from unstructured text and build machine learning models on text data
  • Syntax and Semantics: Conduct sentiment analysis, learn to parse English sentences and extract meaning from them
  • Other problems in text analytics: Explore the applications of text analytics in new areas and various business domains
  • Information flow in a neural network: Understand the components and structure of artificial neural networks
  • Training a neural network: Learn the cutting-edge techniques used to train highly complex neural networks
  • Convolutional Neural Networks: Use CNN's to solve complex image classification problems
  • Recurrent Neural Networks: Study LSTMs and RNN's applications in text analytic
  • Creating and deploying networks using Tensorflow and keras: Build and deploy your own deep neural networks on a website, learn to use the Tensorflow API and Keras
  • Directed and Undirected Models: Learn the basics of directed and undirected graphs
  • Inference: Learn how graphical models are used to draw inferences using datasets
  • Learning: Learn how to estimate parameters and structure of graphical models
  • Introduction to RL: Understand the basics of RL and its applications in AI
  • Markov Decision Processes: Model processes as Markov chains, learn algorithms for solving optimisation problems
  • Q-learning: Write Q-learning algorithms to solve complex RL problems

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