PG Program in

Machine learning & Deep Learning

Online6 MonthsRs. 1,00,000 (Excl. Taxes)

In Association with

Complete a Rigorous Post-Graduate Program

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

Program Vitals

Program Fee

Rs. 1,00,000
EMI starts at INR 2,882/- month.
(Exclusive of all taxes)
View Plans

Course Duration

Jan'19 - Jul'196 months

Time Commitment

10 hoursper week

Program Syllabus

The curriculum has been developed by IIIT Bangalore and Deep Learning companies. This program will teach you end to end skills - a thorough understanding of fundamental concepts and thinking beyond tools.

  • INTRODUCTION TO PYTHON - Get acquainted with Data Structures and Object-Oriented Programming
  • PYTHON FOR DATA ANALYSIS - Learn how Python is used for Data Manipulation and Data Visualization
  • MATH FOR DATA ANALYSIS - Brush up your knowledge of Linear Algebra, Matrices, Eigen Vectors and their application for Data Analysis

To learn more about why should you be taking prep sessions, click here

Duration: 8 weeks

In this course, you will be given an introduction to Statistics. You will also develop an important foundation and know how to formulate hypotheses to solve business problems. 

Topics Covered:

  • 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
  • INVESTMENT CASE STUDY (INDIVIDUAL PROJECT) Recommend Investment Strategies to Spark Funds using data manipulation and analysis.

Duration: 8 weeks

Topics Covered:

  • LINEAR REGRESSION - Learn to implement linear regression and predict continuous data values
  • LOGISTIC REGRESSION - Understand how supervised learning is used for classification Introduction to logistic regression & multivariate logistic regression with model evaluation
  • NAIVE BAYES -  Learn about Naïve Bayes classifier and its uses with continuous data and learn
  • CLUSTERING  - Introduction to clustering -Learn how to create segments based on similarities using K-Means and Hierarchical clustering

Topics Covered :

  • PRINCIPAL COMPONENT ANALYSIS (PCA) -  Create and implement principal components with clusters implementation using PCA in Python
  • SUPPORT VECTOR MACHINES -  Learn the concept of hyperplanes and classify data points using support vectors. Understand the usage of SVM in sklearn. 
  • DECISION TREES - Fundamentals of tree-based model that is simple and easy to use. Learn algorithms for Decision Tress construction & implementation
  • ENSEMBLE – BAGGING & BOOSTING - Create an Ensemble – Bagging, Gradient boosting and Random Forests
  • INTRODUCTION TO NEURAL NETWORKS - Understand the components and structure of artificial neural networks.
  • CONVOLUTIONAL NEURAL NETWORKS (CNNs) - Understand the architecture of complex CNNs like softmax layers, maxpools and use CNNs to solve complex image classification problems
  • RECURRENT NEURAL NETWORKS - Study LSTMs and RNNs application in text analytics with industry examples
  • TENSORFLOW & KERAS -  Learn to create and deploy neural networks using Tensorflow & Keras
  • GROUP PROJECT -  Build and deploy a neural network – Create an object detection model and deploy it on a web application.

* signifies optional/additional learning material for interested students

You will receive the download link in your email.