Online11  months Rs 2,85,000 (incl. all taxes)

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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

Program Vitals

Program Fee

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

Course Duration

Jan'19 - Dec'1911 months

We recommend

15 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

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