Executive PG Program in Machine Learning & Artificial Intelligence
Course Information
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Overview
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Learning Outcomes
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Who Should Attend
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Testimonials
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Trainer's Profile
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Course Outline
Learn in-demand skills such as Deep Learning, NLP, Reinforcement Learning, work on 12+ industry projects & multiple programming tools. Learn through real-life industry projects sponsored by top companies across industries
Engage in collaborative real-life projects with student-expert interaction, benefit by learning in-person with Industry Experts. Gain personalized subjective feedback on your submissions to facilitate improvement.
You will learn how to1. Cover industry projects such as
- Build a chatbot
- Object Detection in Images
- Speech Recognition
- Image Captions
2. Explore Programming Tools, Languages and Libraries
- Pandaas
- Seaborn
- Flask
Minimum Eligibility:
- Bachelor’s Degree with 50% or equivalent passing marks.
- Minimum 1 year of work experience in a technical domain or a degree in mathematics or Statistics with programming experience.
“After just 6 months of starting my AI and ML journey, I have been rewarded with a Data Scientist position in Tech Mahindra with a very hefty package and this is just after going halfway through the program. I can put my money on this program on any given day and assure everyone that this is one program that stands apart from every other in the market. The program pedagogy is impeccable but what I liked the most is the constant motivation from my student mentor which kept me going in the program.”Saurav Kant Kumar, Experience: 3.6 Years
Hindol Basu
CEO, Actify Data Labs
An alumnus of IIT and IIM with over 13 years of experience in analytics with industry leaders such as Citigroup and Tata Industries.
Chandrashekar Ramanathan
Dean Academics, IIITB
Prof. Chandrashekar has a PhD from Mississippi State University and experience of over 10 years in several multinational organisations.
Pre-Program Preparatory Content (3 Weeks)
- Introduction to Python
- Python for Data Science
- Data Visualisation in Python
- Data Analysis Using SQL (Optional)
- Advanced SQL and Best Practices (Optional)
- Data Analysis in Excel (Optional)
- Analytics Problem Solving (Optional)
- Math for Machine Learning
Statistics and Exploratory Data Analytics (5 Weeks)
- Exploratory Data Analysis
- Cloud Essentials: Intro to Git & Cloud
- Cloud Essentials: Intro AWS
- Investment Assignment
- Inferential Statistics
- Hypothesis Testing
- Lending Club Case Study
Machine Learning - I (7 Weeks)
- Linear Regression
- Linear Regression Assignment
- Logistic Regression
- Naive Bayes
- Model Selection
Machine Learning - II (7 Weeks)
- Advanced Regression
- Advanced Regression Assignment
- Support Vector Machine (Optional)
- Tree Models
- Model Selection - Practical Considerations
- Boosting
- Unsupervised learning: Clustering
- Unsupervised Learning: Principal Component Analysis
- Telecom Churn Case Study
Deep Learning (8 Weeks)
- Introduction to Neural Networks
- Convolutional Neural Networks - Industry Applications
- Convolutional Neural Networks - Assignment
- Recurrent Neural Networks
- Neural Network Project - Gesture Recognition
Natural Language Processing (7 Weeks)
- Lexical Processing
- Syntactical Processing
- Syntactic Processing - Assignment
- Semantic Processing
- Case Study: Classifying Customer Complaint Tickets
Elective 1: DL with MLops (10 Weeks)
- Cloud Essentials: Intro to AWS
- Working with AWS: case study
- MLOps: Introduction
- MLOps: Data Lifecycle
- MLOps: Model Lifecycle
- MLops Assignment
- Advanced CV
- Advanced CV
- MLOps + Deployment: DL (Theory)
- MLOps + Deployment: DL (assignment)
Elective 2: NLP with MLops (10 Weeks)
- Cloud Essentials: Intro to AWS
- Working with AWS: case study
- MLOps: Introduction
- MLOps: Data Lifecycle
- MLOps: Model Lifecycle
- MLops Assignment
- Advanced NLP
- Advanced NLP
- MLOps + Deployment: NLP (Theory)
- MLOps + Deployment: NLP (assignment)
Elective 3: AI Strategy (10 Weeks)
- Cloud Essentials: Intro to AWS
- Working with AWS: case study
- MLOps: Introduction
- MLOps: Data Lifecycle
- MLOps: Model Lifecycle
- MLops Assignment
- AI Strategy Framework, Structured Problem Solving/ Data Storytelling
- Mapping ML with Data architecture strategy
- Executing AI Strategy
- AI strategy: Assignment
- Capstone
Reinforcement Learning (Optional)
- Classical Reinforcement Learning
- Assignment - Classical Reinforcement Learning
- Deep Reinforcement Learning
- Reinforcement Learning Project
