Master of Science in Machine Learning & AI
Our best-selling program with a 4.5-star rating. Learn in-demand skills such as Deep Learning, NLP, Reinforcement Learning & work on 12+ industry projects, multiple programming tools & a dissertation.
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With Industry Experts From
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40,000+Recommended 15 hours/week
Fully OnlineLearning Duration
20 MonthsLearn about ChatGPT and the latest trends in AI
Advanced AI Lecture SeriesCountries Learner Base
85+ countriesProgram Start Date
Coming SoonAccredited from Institute of Analytics
Program Overview
Key Highlights
20 In-Demand Tools & Languages
30+ Case Studies, Coding Assignments & Capstone Project
4 Months Masters Thesis
Expert Coaching Sessions
Global Industry Experts
500+ Hours of Learning
Student Support available all day for your convenience (24*7). For urgent queries, use the Call Back option on the platform
IIIT Bangalore & LJMU Alumni Status
Career Essential Soft Skills Program
Timely Doubt Resolution
Accredited by Institute of Analytics UK (IOA)
Eligibility:
- 50% (or equivalent) Bachelor's Degree preferably in a Mathematical/ Statistical background.
- Recommended 1 year of work experience in programming/ analytics.
Syllabus
Experience 500+ hours of best-in-class content developed by leading faculty & industry leaders in the form of videos, case studies & projects.
Download Syllabus
20+ Programming Languages, Tools & Libraries Covered
Master's Degree from LJMU
With a heritage that stretches back to 1823, Liverpool John Moores University, UK is now one of the largest and most well-established universities in the UK. It has been ranked in the Top 100 World Young Universities & Top 50 in the UK by Student Satisfaction.
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- Complete all courses to achieve this prestigious M.Sc. Degree from LJMU, UK to jump-start your career in ML & AI
- Get access to the complete digital library of LJMU to research & write your dissertation
- Earn a Master's degree which is recognised by WES, at 1/10th the cost of an offline program
Instructors
Learn from leading ML & AI faculty and industry leaders
Dr. Gabriela Czanner
Faculty - Engineering and Technology
A Senior Lecturer in Statistics and Data Science at Department of Applied Mathematics at LJMU. Her research focus is advanced statistics for decision support.
Dr. Atif Waraich
Faculty - Computer Science
A Senior faculty of Engineering and Technology at LJMU who has multiple publications in the healthcare domain
Anshuman Gupta
Director | Data Science
He has a Ph.D. (Dual) from Penn State University as well as a B.Tech. Degree from IIT Bombay.
Prof. Dhiya Al-Jumeily
Professor - Artificial Intelligence
A Senior Member of the IEEE and a Chartered IT Professional. He is a fellow of the UK Higher Education Academy.
Ankit Jain
Sr. Research Scientist
An alumnus of IIT Bombay, UCB, and Harvard Business School with over 9 years of experience.
Prof. Paulo Lisboa
Head of Dept - Applied Mathematics
Studied mathematical physics at LU and was the chairman of Industrial Mathematics at LJMU in 1996 and Head of Graduate School in 2002
Chandrashekar Ramanathan
Dean | Academics
Prof. Chandrashekar has a Ph.D. from Mississippi State University and over 10 years of experience in several multinational organisations.
Tricha Anjali
Ex-Associate Dean
Prof. Anjali has a Ph.D. from Georgia Tech as well as an integrated M.Tech. (EE) from IIT Bombay. She is currently the dean of IIITB.
Prof Debabrata Das
Director, IIITB
He is PI and nodal officer of the project under the National Mission for Interdisciplinary Cyber-Physical Systems.
Prof. G. Srinivasaraghavan
Professor
Prof. Srinivasaraghavan has a Ph.D. in Computer Science from IIT Kanpur and 18 years of experience with Infosys Technologies.
Ujjyaini Mitra
Head of Analytics
An alumnus of McKinsey and Co., Flipkart, and Bharti Airtel with over 11 years of experience.
Mirza Rahim Baig
Lead | Business Analytics
Advanced analytics professional with 8+ years of experience as a consultant in the e-commerce and healthcare domains.
Prof. V. Sridhar
Faculty in Charge, CPE, IIITB
He has a Ph.D. from the University of Iowa, U.S.A. He has been a member of GOI committees on Telecom & IT.
Syllabus
Experience 750+ hours of best-in-class content developed by leading faculty & industry leaders in the form of videos, case studies & projects
Download Brochure
750+
Hours of Content12+
Case Studies and Projects40+
Live Sessions11
Coding Assignments20
Tools, Languages & Libraries6
Capstone Projects to Choose FromInternational Preparatory Course
4 Weeks
- Excel MasterClass
- Overview of DS/ML/AI
- Data Visualisation and StoryTelling
- Data Science Problem Solving
- Introduction to Python
- Python for Data Science
- Data Visualisation in Python
- Analytics Problem Solving (Optional)
- Data Analysis Using SQL (Optional)
- Advanced SQL (Optional)
- Programming using Python - I(Optional)
- Programming using Python - II (Optional)
- Data Analysis in Excel (Optional)
- Math for Machine Learning (Optional)
Statistics and Exploratory Data Analytics
5 Weeks
- Exploratory Data Analysis
- Cloud Essentials: Intro to Git & Cloud
- Investment Assignment
- Inferential Statistics
- Hypothesis Testing
- Lending Club Case Study
Machine Learning - I
7 Weeks
- Linear Regression
- Assignment: Linear Regression
- 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 Networks 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
- 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
- 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
Research Methodologies
8 Weeks
- Introduction to Research and Research Process
- Research Design
- Literature Reviewing
- Research Project Management
- Report Writing and Presentation Skills
- Scientific Ethics
Master's Dissertation
16 weeks
- 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
AI Mastery Syllabus
Dive into the latest trends like ChatGPT, Dall E, and Explainable AI (XAI) through this series by renowned experts in the field.
Unleashing the Power of AI: Exploring the Latest Trends in Generative Modelling
The GPT-3 Revolution
- Introduction to ChatGPT: The world of AI is constantly evolving, and staying up to date with the latest advancements can be challenging. This session will introduce you to the power and potential of GPT-3.
- Objective: To comprehensively understand GPT-3, its capabilities like Natural Language Understanding(NLU) & Context Recognition, and how it can be used in their work or business.
Be 10x productive with Prompt Engineering
- Prompt engineering is a powerful technique that can help content creators, freelancers, and businesses generate high-quality text quickly and efficiently. This session will introduce you to the concept of prompt engineering and its applications.
- Objective: The objective of this session is to provide learners with a comprehensive understanding of prompt engineering and how it can be used with different AI tools like ChatGPT3, Midjourney & Stable Diffusion.
- By the end of the session, learners will be able to use prompt engineering techniques to generate high-quality text for a variety of applications.
Crack the Code of AI: Demystifying Model Explainability and Interpretability
From Black Box to Glass Box: An introduction to Explainable AI
- With the increasing adoption of AI, there is a growing need to ensure that AI models can be understood and trusted. This session will introduce you to the concept of Explainable AI and Model Interpretability.
- Objective: To gain a comprehensive understanding of the importance of XAI (Explainable Aritificial Intelligence) and model interpretability, and learn how to build and interpret an interpretable machine learning model using LIME & SHAP techniques
Is AI racist and biased?
- As AI is increasingly used in various domains, it is important to consider the ethical implications and potential biases that can arise. This session will introduce learners to the concepts of ethics and bias in AI and provide real-world examples.
- Objective: To understand the ethical and bias considerations in AI and how to identify and mitigate potential issues. Learn through case studies in healthcare, finance, autonomous vehicles, recruitment among others.
Building Innovative Products with AI: Leveraging the Power of Generative Modelling
From Data to Dialogue: Diving into ChatGPT's Architecture and Functionality
- This session will provide an overview of GPT models and their architecture, along with advanced techniques like RLHF and InstructGPT.
- Objective: The objective of this session is to introduce learners to one of the variant of the Transformer - the GPT model, specifically the Davinci variant. They will gain knowledge about the architecture of GPT models and advanced techniques such as Reinforcement Learning from Human Feedback (RLHF), one-shot & few shot learning. They will also learn how to fine-tune GPT models with prompts and evaluate their performance through hands-on exercises.
ChatGPT-Based Ticket Classifier: A Game-Changer for Support Teams
- Using transfer learning with GPT-3 eliminates the need to train NLP models from scratch, and provides access to state-of-the-art language models on various NLP tasks. This session will help you perform 'Ticket Classification Using ChatGPT'
- Objective: To build a product that can automatically classify support tickets into different categories based on their topics using the fine-tuned ChatGPT model. This will help companies and organizations to improve their support ticket handling efficiency and reduce the workload of their support teams.
Build an Image generator using Stable Diffusion
- Using transfer learning with GPT-3 eliminates the need to train NLP models from scratch, and provides access to state-of-the-art language models on various NLP tasks. This session will help you 'Build an image generator using stable diffusion'
- Objective: The objective of this session is to teach learners how to build an image generator using Stable Diffusion and the Hugging Face library. They will learn how to prepare data, build and train a Stable Diffusion model, evaluate its performance, and build a Streamlit dashboard to showcase the model's output.
Innovations in Graph Networks, Computer Vision and Generative Modelling
The world of Graph Networks
- This session aims to simplify the process and provide learners with a comprehensive understanding of Graph Neural Networks (GNNs) and how to build a simple GNN model.
- Objective: The objective of this session is to introduce learners to the components of a GNN, its working mechanism, and how to build a simple GNN model for a given problem statement. By the end of the session, learners will be able to apply their knowledge to solve problems related to graph data.
Advanced Graph Networks
- With the limitations of simple GNNs, there is a need to explore advanced GNN architectures. This session aims to provide an in-depth understanding of advanced graph networks.
- Objective: By the end of this session, learners should be able to comprehend the drawbacks of simple GNNs. Additionally, learners should be able to compare and evaluate the performance of advanced GNNs like Graph Convolutional Networks(GATs) & Graph Attention Networks(GANs) to simple GNNs.
State of Art (STOA) architectures in Computer vision
- In this session, we will introduce learners to the concept of landmark and feature detection, and enable them to build a simple landmark detection system.
- Objective: By the end of the session, learners will be able to understand the fundamentals of landmark detection, popular feature detection algorithms such as SIFT and SURF, and build a landmark detection system using feature detection algorithms.
State of Art(STOA) architectures in generative modelling
- Keeping up with the latest advancements in generative modeling can be challenging. This session aims to provide an overview of the state-of-the-art (STOA) architectures in generative modeling and their applications.
- Objective: By the end of the session, learners should have an understanding of the STOA architectures in generative modeling and be able to read research papers on topics such as image-to-text models (Vision Transformers), text-to-image models(CoCA, CLIP), and multimodality(Pathways).
Industry Projects
Learn through real-life industry projects sponsored by top companies across industries
- Engage in collaborative projects with student-mentor interaction
- Benefit by learning in-person with Expert Mentors
- Personalized subjective feedback on your submissions to facilitate improvement
Telecom Churn Analysis
Solve the most crucial business problem for a leading telecom operator in India and southeast Asia - predicting customer churn.
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Train an agent to play Tic Tac Toe
Learners will apply Q-Learning to train an RL agent to play the game of numerical Tic Tac Toe.
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Classification of Customer Complaints
Create a solution that will help in identifying the type of complaint ticket raised by the customers of a multinational bank
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Credit Card Fraud Detection
Build a machine learning model capable of detecting fraudulent transactions. Here you have to predict fraudulent credit card transactions with the help of machine learning models.
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Melanoma Detection Assignment
Build a neural network from scratch in Tensorflow to identify the type of skin cancer from image.
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Gesture Recognition
Make a Smart TV system which can control the TV with user’s hand gestures as the remote control.
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News Recommender System
Build a model to using the concepts of natural language processing and recommender systems to recommend news stories to users on a popular news platform.
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Maximising profit of cab driver using RL
Learners will use the Markov Decision Process & Q-Learning to build an RL agent that learns to choose the best request so as to maximize the total profit earned by the agent that day.
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Custom Entity detection in Healthcare data
You will build a custom NER to get the list of diseases and their treatment from a medical healthcare dataset.
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Eye for the Blind
Build a model that can help any visually impaired person in understanding image present before them. It is a deep learning model which can explain the content of an image in the form of speech.
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Sentiment Analysis based Product Recommender System
Build a sentiment analysis based product recommendation system to recommend the similar products to the users. Sentiment analysis is used to fine tune the product recommendation system.
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Sales Forecasting
Predict the sales for a european pharma giant using a host of different types of variables. Apply VAR and VARMAX models to build the appropriate model
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Style Transfer using GAN's
Build a Model for converting MRI images from one type (T1) into other (T2) and vice versa. CycleGAN model is used for producing T2 type MRI images given T1 type input MRI images
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Machine Translation System
You will build an attention-based neural machine translation model translating from one language to another.
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Face Mask Detection
Create a custom object detector using the YOLO algorithm to detect the presence of face masks in the images of different people.
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Benefits with upGrad
360 Degree Career Support services, Personalized Mentorship from Industry Experts, Hands-on Projects, Peer Networking opportunities & a whole lot more to help you master Machine Learning & AI.
Career Assistance
Interview Prep & Resume Building
- Get 1:1 Resume Review Sessions with Industry Experts & a dedicated Career Mentor
- Profile Building Workshops: Live sessions on how to build your profile - be it your resume or GitHub profile
- Explore 100+ commonly asked interview questions across modules
Company Specific Preparation
- Just-in-Time Interviews: Get company and role-specific preparation with mock interviews right before your actual interviews
- Access to a carefully curated pool of interview resources per company to ensure you're interview-ready
Learning Support
Industry Expert Mentorship
- Fortnightly personalised group (1:8) mentorship sessions with industry experts for pro-active mentoring
- Calls with industry experts for personalised feedback & guidance spread over 3 months
- Live interactive sessions with leading industry experts covering curriculum + advanced topics
Student Support
- Available all days 24 hours.
- For urgent queries, use the Call Back option on the platform.
Practical Learning & Networking
Hands-on Project
- 12+ industry projects & assignments and choose 6 options out of 10 Capstone Projects
- Live coding classes on Kaggle & OpenCV & sessions to build your Github profile
Networking & Learning Experience
- Live Discussion forum for peer to peer doubt resolution monitored by technical experts
- Reverse knowledge transfer sessions (FLIP classrooms) with learners tutoring fellow batchmates
- Peer to peer networking opportunities with a alumni pool of 10000+
- Lab walkthroughs of industry-driven projects
Our Learners Work At
Top companies from all around the world have recruited upGrad alumni
Admission Process
There are 3 simple steps in the Admission Process which are detailed below:
Step 1
Online eligibility test
Complete your application and take a quick 40 minutes online test to start the admission process with 18 aptitude questions.
Step 2
Get shortlisted & receive offer letter
Admissions Committee reviews the test score & profile to shortlist learners and send offer letters to.
Step 3
Confirm your seat
Reserve your seat by paying the enrolment amount to enrol in the program.
Program Fees
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Refer someone you know and receive cash reimbursements of up to $500!*
*More details under the referral policy under Support Section
Empowering learners of tomorrow
Over 2,300 students have completed this course and started working at their dream job, whats stopping you?
Student Reviews
Sondre Rennan Nesset
The highlights of my course so far are the depth of knowledge it has given me about AI/ML, the assignments which have taught me more about the field as well as the affordability of the course.
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Budhaditya Goswami
Topics are designed keeping practical application at the forefront which means that learners practice on actual problems in real industries. The content, the reputation of the provider and course fees being within my budget are why I chose upGrad.
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Krishnan Sivasubramanian
I was referred by a friend and decided to give it a shot. Best investment.
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K M Kadir Khan
I feel lucky to have stumbled upon this course. It is the best investment I've made on myself.
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Emmanuel Idehen
Very happy that I joined the program for upskilling in ML & AI & upGrad gave me the right opportunity.
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Ankit Bansal
I really appreciate upGrad's online education system and providing valuable certification.
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Ankit Jain
More than a quest for career transition it was curiosity that drove me to this course. I am still learning but equipped with better tools.
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Nishant Chalasany
upGrad helped me establish communication channels with the Data Science & Data Platforms teams & ensure we worked towards high quality deliveries for my organization.
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Jyothi Rajesh
The upGrad experience of lectures, live sessions, MCQs, individual and group assignments and exams was overwhelming, but rewarding as I covered topic-after-topic.
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Ridhiman Singh
Having completed my PGDMLAI from IIITB, I started searching for opportunities and it didn't take much time to find one with all the knowledge and skills.
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Frequently Asked Questions
Program Details
How do I know if this program is for me?
This program is for you if you are a:
Data Scientist or Senior Data Analyst: If you are comfortable with data wrangling, have implemented statistical or machine learning models in the past, and have spent at least 2 years as a working professional. You should also have some experience with R/Python/Scala.
Statistician: If you received formal education in statistics or mathematics and have at least 2 years of working experience.
Data Engineer/Big Data Engineer: If you have at least 2 years of experience in creating data pipelines/handling data warehouses and you have created ETL procedures. In addition, you should be familiar with various frameworks and tools like Hadoop and Spark.
Software Developer: If you have worked on creating and deploying software/apps for at least 4 years, you are comfortable with object-oriented programming and know C/C++/Java/Python or a similar language.
Data Scientist or Senior Data Analyst: If you are comfortable with data wrangling, have implemented statistical or machine learning models in the past, and have spent at least 2 years as a working professional. You should also have some experience with R/Python/Scala.
Statistician: If you received formal education in statistics or mathematics and have at least 2 years of working experience.
Data Engineer/Big Data Engineer: If you have at least 2 years of experience in creating data pipelines/handling data warehouses and you have created ETL procedures. In addition, you should be familiar with various frameworks and tools like Hadoop and Spark.
Software Developer: If you have worked on creating and deploying software/apps for at least 4 years, you are comfortable with object-oriented programming and know C/C++/Java/Python or a similar language.
How will this program benefit me?
The program will benefit you in different ways depending on your prior experiences:
Data Scientist or Senior Data Analyst: The program will familiarise you with the advancements in ML and AI. It will also help you understand the mathematics behind algorithms and how you can modify them to suit your needs so that you can transition to a Senior Data Science or Machine Learning role.
Statistician/Mathematician: Apart from familiarising you with the advancements in ML and AI, the program will help you understand how to define a structured approach to solve a business problem, and equip you with the right tools so that you can transition to a Machine Learning or Data Science role.
Data Engineer/Big Data Engineer: The program will set up a solid foundation of Statistics, Machine Learning, and AI along with problem-solving skills so that you can solve enterprise-level problems. It will build upon your existing knowledge of various tools to make you a Full-Stack Machine Learning or Data Science professional.
Software Developer: The program will help you create a strong foundation of Statistics, Machine Learning, and Business Understanding. It will leverage your existing knowledge of programming and expand the technologies you are familiar with so that you can become a well-rounded Machine Learning professional.
Data Scientist or Senior Data Analyst: The program will familiarise you with the advancements in ML and AI. It will also help you understand the mathematics behind algorithms and how you can modify them to suit your needs so that you can transition to a Senior Data Science or Machine Learning role.
Statistician/Mathematician: Apart from familiarising you with the advancements in ML and AI, the program will help you understand how to define a structured approach to solve a business problem, and equip you with the right tools so that you can transition to a Machine Learning or Data Science role.
Data Engineer/Big Data Engineer: The program will set up a solid foundation of Statistics, Machine Learning, and AI along with problem-solving skills so that you can solve enterprise-level problems. It will build upon your existing knowledge of various tools to make you a Full-Stack Machine Learning or Data Science professional.
Software Developer: The program will help you create a strong foundation of Statistics, Machine Learning, and Business Understanding. It will leverage your existing knowledge of programming and expand the technologies you are familiar with so that you can become a well-rounded Machine Learning professional.
What is this program intended to do?
This program intends to produce extremely well-rounded data scientists and AI professionals with deep knowledge of mathematics, expertise in relevant tools/languages, and understanding of cutting-edge algorithms and applications.
What can I expect out of this program?
This program is designed for working professionals looking to pick up skills in advanced concepts like Reinforcement Learning, Graphical Models, NLP, Deep Learning along with a solid foundation of Statistics. This program demands consistent work and time commitment over the entire duration of 20 months.
What should I NOT expect from this program?
This program is NOT intended to serve as an entry point to the field of Data Science. It is aimed at developing professionals who can be absorbed into Senior Data Scientist and Machine Learning/AI roles. Our Master's Degree in Machine Learning Program is aimed at facilitating the transition of professionals to the Machine Learning field.
Is there any certification at the end of the program?
Post completion of the program, a certificate in Executive PG Program in Machine Learning and AI will be granted from IIIT Bangalore and a Master's Degree from LJMU.
Support
What type of learning should I expect?
The content will be a mix of asynchronous lectures from industry leaders as well as world-class faculty. Additionally, the program comprises of some live lectures or hangout sessions dedicated to solving your academic queries and to reinforce learning.
Selection Criteria
What is the selection process for this program?
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
Refund Policy/Financials
Is there any deferral or refund policy for this program?
Disclaimer
1. upGrad does not grant credit; credits are granted, accepted or transferred at the sole discretion of an educational institution. upGrad does not make any representations regarding the recognition or equivalence of the credits or credentials awarded, unless otherwise expressly stated. If you intend to pursue a post graduate or doctorate degree upon completion of this course or apply for employment which requires specific credits, we advise you to enquire further regarding the suitability of this degree for your academic and/or professional requirements before enrolling.
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