Master of Science in Machine Learning & AI

Course Overview

• Gain an M.Sc. degree from LJMU, UK & also get a Executive PG Diploma from IIITB on successful completion of the program.
• Learn with the ease and flexibility of recorded sessions as well as live sessions – designed to ensure a wholesome learning experience.
• Learn and apply concepts on industry projects and work on a Capstone project along with personalised industry mentorship
• Get mentored by a thesis supervisor for your Masters Thesis & gain complete command on the subject.
• Receive career services in the form of career coaching, resume building, career mentorship from Global Industry Experts and much more.
• Gain alumni status from LJMU, UK & IIITB when you complete the program.

Program Outline/Syllabus

• 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)
• EXPLORATORY DATA ANALYSIS
• INVESTMENT ASSIGNMENT
• INFERENTIAL STATISTICS
• HYPOTHESIS TESTING
• LENDING CLUB CASE STUDY
• LINEAR REGRESSION
• ASSIGNMENT: LINEAR REGRESSION
• LOGISTIC REGRESSION
• NAIVE BAYES
• MODEL SELECTION
• 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
• LEXICAL PROCESSING
• SYNTACTICAL PROCESSING
• SYNTACTIC PROCESSING – ASSIGNMENT
• SEMANTIC PROCESSING

What will I learn?

• 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)
• EXPLORATORY DATA ANALYSIS
• INVESTMENT ASSIGNMENT
• INFERENTIAL STATISTICS
• HYPOTHESIS TESTING
• LENDING CLUB CASE STUDY
• LINEAR REGRESSION
• ASSIGNMENT: LINEAR REGRESSION
• LOGISTIC REGRESSION
• NAIVE BAYES
• MODEL SELECTION
• 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
• LEXICAL PROCESSING
• SYNTACTICAL PROCESSING
• SYNTACTIC PROCESSING – ASSIGNMENT
• SEMANTIC PROCESSING

Admission Key Facts:

50% (or equivalent) Bachelor’s Degree preferably in a Mathematical/ Statistical background. Minimum 1 year of work experience in a technical domain or a degree in mathematics or Statistics with programming experience.

Enrolment process

• upGrad, IIITB, LJMU, world-class faculty, and many industry leaders have committed a lot of time in conceptualising and creating this program to make sure that the candidates can receive the best possible learning experience. Hence, we want to make sure that the participants of this program also show a very high level of commitment and passion for Machine Learning and AI.
• The applicants will have to take a selection test designed to check their mathematical and programming abilities. The applicants can skip the test if they meet the following criteria:
• 50% (or equivalent) Bachelor’s Degree preferably in a Mathematical/ Statistical background. Minimum 1 year of work experience in a technical domain or a degree in mathematics or Statistics with programming experience.

What do I need?

50% (or equivalent) Bachelor’s Degree preferably in a Mathematical/ Statistical background. Minimum 1 year of work experience in a technical domain or a degree in mathematics or Statistics with programming experience