Machine Learning and AI

Online11 MonthsRs. 2,85,000 (Incl. Taxes)

In Association with

Successfully launched

5 batches
250+Recruitment partners

Our Student Profile

Job Profiles
image icon
Software Developers & Engineers
image icon
Data Analysts, Engineers & Scientists
Work Experience
work experience graph
Companies they work at

Complete a Rigorous Post-Graduate Program

Complete all courses successfully and receive Post-Graduate certificate. Become a part of the Machine Learning community with the PG Alumni status 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

Program Fee

Rs. 2,85,000
EMI starts at INR 9,952/- month.
(Inclusive of all taxes)
View Plans

Course Duration

Dec'18 - Nov'1911 months

We recommend

10 hoursper week

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

You will receive the download link in your email.

Student Reviews