School of Design & IT

AI & Machine Learning Program

Eligibility: Minimum 1year of Work Experience + Bachlor’s Degree with Minimum 50%

What you’ll learn

Program Summary

Course Key Features

Programming Tools Covered

Take a Tour of Our Education Platform

Discover a personalized learning experience with our cutting-edge platform powered by AI technology. Get access to top-quality educational content, live interactive sessions, and guidance from renowned industry leaders.

Meet Our Instructors

Gain knowledge from prominent ML & AI experts in India's academic and industrial sectors.


Mr. Sameer, a professor, holds multiple degrees from reputable universities, including a doctorate, postgraduate, and undergraduate degree. He also has extensive experience working for various multinational organizations, spanning over 18 years.

Rahul CR

Rahul CR is an accomplished Senior Data Analyst with a decade of experience in the fields of Statistics, Machine Learning, Software, and Information Technology. He holds both a Bachelor's and Master's degree in Information Technology.

Program Curriculum

Gain access to over 100+ hours of exceptional educational material created by top-notch educators and industry experts, which includes engaging video lectures, insightful case studies, and practical projects.

Hours of Learning
15+ Case Studies & Assignments
Live Sessions
6+ Practical Hands-on
Capstone Projects

Pre Program preparatory content (1 Week)

  • Introduction to Python
  • Python for Data Science
  • Data Visualization in Python
  • Data Analysis Using SQL (Optional)
  • Advanced SQL and Best Practices (Optional)
  • Data Analysis in Excel
  • Analytics Problem Solving
  • Math for Machine Learning

Statistics & Exploratory Data Analytics (2 Weeks)

  • Exploratory Data Analysis
  • Cloud Essentials: Intro to Git & Github
  • Inferential Statistics
  • Hypothesis Testing
  • Lending Club Case Study

Machine Learning - I

  • Linear Regression
  • Assignment
  • Logistic Regression
  • Naive Bayes
  • Model Selection

Machine Learning - II

  • Advanced Regression
  • Advanced Regression Assignment
  • Support Vector Machine (Optional)
  • Tree Models
  • Model Selection – Practical Considerations
  • Boosting
  • Unsupervised learning: Clustering
  • Unsupervised Learning: Principal Component Analysis

Deep Learning

  • Introduction to Neural Networks
  • Convolutional Neural Networks – Industry Applications
  • Convolutional Neural Networks – Assignment
  • Recurrent Neural Networks
  • Neural Network Project – Gesture Recognition

Natural Language Processing

  • Lexical Processing
  • Syntactical Processing
  • Syntactic Processing – Assignment
  • Semantic Processing
  • Case Study: Classifying Customer Complaint Tickets

AI Strategy (Elective)

  • Cloud Essentials: Intro to AWS
  • Working with AWS: case study
  • MLOps: Introduction
  • MLOps: Data Lifecycle
  • MLOps: Model Lifecycle
  • AI Strategy Framework, Structured Problem Solving/ Data Storytelling
  • Mapping ML with Data architecture strategy


  • News Recommended System
  • Credit Card Fraud Detection
  • Eye for Blind – (Image Captioning)
  • Sentiment analysis based Product Recommender system
  • Sales forecasting
  • Style Transfer using GAN’s

Reinforcement Learning (Elective)

  • Classical Reinforcement Learning
  • Assignment -Classical Reinforcement Learning
  • Deep Reinforcement Learning
  • Reinforcement Learning Project

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