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Master of Science in Machine Learning & AI

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Master of Science in Machine Learning & AI

The Master of Science in Machine Learning & AI program is a comprehensive course designed to equip students with advanced skills and knowledge in the rapidly evolving fields of machine learning, artificial intelligence, and data science.

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2 students enrolled

Requirements

Mathematics and Statistics, Analytical and Problem-Solving Skills, Foundational Knowledge in Machine Learning and AI

Description

Master of Science in Machine Learning & AI

Overview

Learn in-demand AI skills such as Deep Learning, Generative AI, NLP & Reinforcement Learning.

Duration	18 months
Total Fee	₹5,60,000
Mode of learning	Online
Difficulty level	Beginner
Official Website	Go to Our Course
Credential	Certificate

Master of Science in Machine Learning & AI

Highlights

Discover More

✓ No Cost EMI Options Available
✓ Dedicated Student Support
✓ Exclusive Job opportunities Portal
✓ 1 Free Deferral
✓ Daily Doubt Resolution Support

✓ Complimentary Python Programming Bootcamp
✓ Designed for Working Professionals
✓ 15+ Case Studies and Projects

Master of Science in Machine Learning & AI
Course Details

Skills you will learn

Excel, Pandas, Python, numpy, Deep Learning, Generative AI, Data Analysis, visualization, Machine Learning

More about this course

✓ Generative AI integrated modules, tools, and projects
✓ Live and Pre-recorded sessions with Top LJMU, IIIT-B faculty, and industry experts
✓ Mock Interviews with Hiring Managers
✓ Hassle-free, No Cost EMI Options Available
✓ Exclusive Job Portal and AI Profile Builder

Master of Science in Machine Learning & AI
Curriculum

Course 1 - Introduction

✓ EDA + Welcome
✓ Intro to Git & Github
✓ Lending club case study
✓ Inferential Stats
✓ Hypothesis Testing

Course 2 - Linear Regression

✓ Linear Regression Module
✓ Linear Regression Module
✓ Linear Regression Assignment: Bike sharing company(demand regression)
✓ Logistic Regression
✓ Naive Bayes
✓ Model Selection
✓ Revision week - Skill test

Course 3 - Advanced Regression

✓ Advanced Regression
✓ Advance regression assignment + SVM (Optional)
✓ Tree Models
✓ Random Forests + Practical Considerations
✓ Boosting - 1
✓ Unsupervised Clustering
✓ PCA
✓ Telecom Churn Case Study
✓ Buffer - Skill test

Course 4 - Neural Networks

✓ Introduction to Neural Networks - Part 1
✓ Introduction to Neural Networks - Part 2
✓ Convolutional Neural Networks
✓ Assignment: Convolutional Neural Networks
✓ CNN: Practical Considerations
✓ Recurrent Neural Networks
✓ Gesture Recognition - Project
✓ Exam on Sunday End of the week
✓ Revision week - Skill test (DL)

Course 5 - NLP

✓ NLP - Lexical Processing
✓ NLP - Syntactic Processing
✓ NLP Assignment - Syntactic Processing
✓ NLP - Semantic Processing
✓ Ticket classification Project
✓ Buffer - Skill test (NLP)

Course 6 - MLOps Elective

✓ Pre-Requisite Module
✓ Pre-Requisite Module
✓ Designing Machine Learning Systems
✓ Experimenting with Data and Model using MLflow
✓ Automating and Orchestrating pipelines with Airflow
✓ Building continuous learning infrastructure
✓ MLOps Project
✓ Advanced NLP - Introduction to Attention Mechanism
✓ Advanced NLP - Introduction to Transformers
✓ Advanced CV - Object Detection & Semantic Segmentation
✓ MLOps + Deployment: DL (Theory)
✓ MLOps + Deployment: DL (Case Study)
✓ Exam on Sunday End of the week
✓ Revision week - Skill test ()

Course 7 - Generative AI Elective

✓ Advanced NLP - Introduction to Attention Mechanism
✓ Advanced NLP - Introduction to Transformers
✓ Fundamentals of Generative AI, ChatGPT & Prompt Engineering using Non Reasoning, Chain of Thought & Advanced Techniques
✓ Product Development using OpenAI APIs, Fine Tuning using STaR technique in Python
✓ Integrating speech using Whisper API and application deployment using Flask
✓ Fundamentals of Multimodal LLMS like Stable Diffusion, Mid Journey
✓ Fundamentals of Design, Photography & Product Development using Stable Diffusion in Python & Applications of LLMs for Data Science problems like clustering, classification
✓ GenAI Project
✓ Applications of LLMs to create Embeddings for Large Documents
✓ Applications of Vectorstores like Pinecone to store and index embeddings of large documents
✓ Understand the importance of LangChain and it's applications
✓ Initialise the agents, tools and vectorstore for storage and retrieval
✓ Connect the components using Chain and explore the power of tools in LangChain
✓ Scale and Deploy Generative AI Apps using Azure OpenAI services
✓ Future Developments in Generative AI

Course 8 - Capstone

✓ Capstone

Course 9 - Research Methodologies

✓ Introduction to Research and Research Process
✓ Research Design
✓ Literature Reviewing
✓ Research Project Management
✓ Report Writing and Presentation Skills
✓ Scientific Ethics

Course 10 - Master's Dissertation

✓ Investigate dietary patterns and metabolite fingerprints of takeaway (fast) food consumers using PCA and clustering methods
✓ Investigate a diagnosis of eye diseases using imaging ophthalmic data
✓ Structure medical images with information geometry
✓ Using Social media feed to place tweets regarding natural disasters on a map
✓ Preventing credit card fraud through pattern recognition
✓ Developing a recommender system for a Media giant
✓ Risk modelling for Financial activities and Investment Banking

Master of Science in Machine Learning & AI
Entry Requirements

Eligibility Criteria

Bachelor’s Degree with minimum 50% or equivalent passing marks, and successful completion of the Executive PG Program in Machine Learning from IIITB. Minimum 1 year of work experience in a technical domain or a degree in mathematics or Statistics with programming experience.