Certification Programme in
Machine Learning with Python: from Linear Models to Deep Learning
Learn from MIT Faculty #1 Globally Ranked (QS 2026) 
Portfolio-ready Capstone Projects solving real business problems 
Gain hands-on expertise with Python, Numpy, Pandas, Matplotlib and production-grade ML libraries Qualifier Test Date
March 1, 2026
Course Duration
7 months
Time Commitment
8-10 Hours/week
Eligibility
12th and above
Learning Mode
Online
2.3K+ students have already registered
Qualifier Test Date
March 1, 2026
Course Duration
7 months
Time Commitment
8-10 Hours/week
Eligibility
12th and above
Learning Mode
Online
Why choose this Programme?
Become Future-Ready in Machine Learning with Industry-Aligned Curriculum and Hands-On Projects
Receive a Certificate of Completion from MITxMicromasters, validating your expertise and boosting your professional credibility.
Access cutting-edge Machine Learning content, interactive simulations, and practical evaluations, with a strong focus on real-world project implementation.
Engage in real-world, case-driven sessions that bridge the gap between theory and practical Machine Learning challenges.
Learn directly from MIT IDSS and gain insights from their research and industry experience.
Resume reviews, career coaching, and placement assistance to support your job search (Eligibility: 65% attendance & 70% overall marks).
Build end-to-end ML systems like digit recognition, NLP models, and recommendation engines to demonstrate your applied expertise.
Prestigious Certification
Receive a Certificate of Completion from MITxMicromasters, validating your expertise and boosting your professional credibility.
Advanced Curriculum
Access cutting-edge Machine Learning content, interactive simulations, and practical evaluations, with a strong focus on real-world project implementation.
Case-Based Learning
Engage in real-world, case-driven sessions that bridge the gap between theory and practical Machine Learning challenges.
World-Class Faculty
Learn directly from MIT IDSS and gain insights from their research and industry experience.
Placement Opportunities: Offered by Masai
Resume reviews, career coaching, and placement assistance to support your job search (Eligibility: 65% attendance & 70% overall marks).
Hands-On Projects
Build end-to-end ML systems like digit recognition, NLP models, and recommendation engines to demonstrate your applied expertise.
What will you learn?
Master the core foundations of Machine Learning and build industry-ready expertise through hands-on projects, simulations, and real-world case studies. Learn to work with data, build and optimize models, apply neural networks, and deploy ML solutions that solve high-impact problems. Gain the skills to excel in ML-driven roles by building a strong portfolio of practical projects, applying advanced techniques, and understanding the full lifecycle of modern AI systems. Become the kind of ML professional companies trust to build accurate, scalable, and innovative AI solutions.
Toolkit
Course Details
Duration
7 Months
Course Mode
Online
Certification
from MITxMicromasters
Module 1: Programming Foundations for AI & Data Science
- • Introduction to Python
- • Python syntax, variables, loops, functions
- • Working with data structures (lists, dictionaries, tuples, sets)
- • File handling
- • Basics of computational thinking
- • Mini-project: Data Exploration with Python
Module 2: Mathematics & Statistics Essentials for Machine Learning
- • Linear Algebra Foundations: vectors, matrices, operations
- • Calculus for ML: functions, gradients, derivatives
- • Probability Theory: random variables, distribution
- • Statistics Essentials: mean, variance, estimation, confidence intervals
- • Intro to Optimization
- • Mini-project: Build a Gradient Descent Simulator
Module 3: Data Handling, Visualization & Practical Tools
- • Numpy, Pandas, Matplotlib & Seaborn
- • Data cleaning, preprocessing & transformation
- • Working with real datasets
- • Feature engineering fundamentals
- • Exploratory Data Analysis (EDA)
- • Mini-project: Real-world Data Analysis Report
Module 4: Foundations of Classical Machine Learning
- • Understanding supervised vs. unsupervised learning
- • Introduction to ML workflow
- • Core ML algorithms:
- Linear Regression
- Logistic Regression
- Decision Trees
- KNN
- K-Means / Clustering
- • Model evaluation metrics
- • Mini-project: Build Your First ML Model
Delivery live by Masai
Module 1: Foundations of Machine Learning
- • Environment Setup + Numpy Exercises
- • Tutorial on Common Scientific Packages
- • Introduction to Machine Learning
- • Linear Classifier & Perceptron
- • Hinge Loss, Margins & Regularization
- • Linear Classification & Generalization
- • Linear Regression
- • Nonlinear Classification
- • Recommender Systems
- • Project 1: Automatic Review Analyzer
Module 2: Deep Learning & Neural Networks
- • Project 2 (Part 1): Digit Recognition
- • Introduction to Feedforward Neural Network
- • Backpropagation & Stochastic Gradient Descent
- • Recurrent Neural Networks (RNNs): Part 1 & 2
- • Convolutional Neural Networks (CNNs)
- • Project 3 (Part 2): Digit Recognition with Neural Networks
Module 3: Unsupervised Learning & Probabilistic Modeling
- • Clustering (K-Means, Hierarchical)
- • Advanced Clustering Techniques
- • Generative Models
- • Mixture Models
- • Expectation-Maximization (EM Algorithm)
- • Project 4: Collaborative Filtering using Gaussian Mixtures
Module 4: Reinforcement Learning & NLP Applications
- • Reinforcement Learning (RL) Fundamentals
- • RL Algorithms & Exploration Strategies
- • Applications in Natural Language Processing
- • Sequence models & text-based tasks
- • Project 5: Text-Based Game (Reinforcement Learning Project)
Through self paced course, live TA support by Masai
Instructors & Industry Experts
Prof. Regina Barzilay
Professor of Electrical Engineering and Computer Science, Massachusetts Institute of Technology.
Linkedin ›Regina Barzilay is a Delta Electronics Professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Mas...
Dr. Harkeerat Kaur is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Jammu, where she teaches and conducts research in areas such as...
SriRam (Sriram) Desai is a seasoned software engineer with experience at leading global technology companies, including roles at PayPal and other major tech firms, bringing strong expertise in backend...
Prof. Regina Barzilay
Professor of Electrical Engineering and Computer Science, Massachusetts Institute of Technology.
Linkedin ›Regina Barzilay is a Delta Electronics Professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Mas...
Dr. Harkeerat Kaur is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Jammu, where she teaches and conducts research in areas such as...
SriRam (Sriram) Desai is a seasoned software engineer with experience at leading global technology companies, including roles at PayPal and other major tech firms, bringing strong expertise in backend...
Admission Process
Clear Qualifier Test
Clear the entrance test to be eligible for the programme
Complete Counselling
Only shortlisted candidates go through the counselling process
Start Learning
Learn from India's top educators and stand out from the crowd
Clear Qualifier Test
Clear the entrance test to be eligible for the programme
Complete Counselling
Only shortlisted candidates go through the counselling process
Start Learning
Learn from India's top educators and stand out from the crowd
Qualifier Test Details
To join this program, you must clear the qualifier test on your allotted date.
Test Topics
- Arithmetic Aptitude
- Data Interpretation
- Logical Reasoning
- Comprehension Skills
Registration Process
- Pay ₹99 and choose your slot
- Unlock free mock test and practice before your final exam
Things to remember
- Test duration : 60 minutes
- You can take the test only once at your allotted time & slot.
- The test must be taken on a desktop or laptop. Supported browsers (latest versions only): Google Chrome, Safari, Microsoft Edge, and Firefox.
Fee Structure
Qualifier Test Fee
(Non Refundable)
Registration Fee
(non-refundable)
Program Fee
(non-refundable)
Total Fees
Option 1
Upfront
(+ GST*)
Option 2
EMI (Through Our NBFC Partners)
(+ GST*)
18% GST extra, as applicable
who is this Programme for?
Anyone with a passion for building a career in Artificial Intelligence and Machine Learning
Advance your AI expertise by mastering machine learning and deep learning techniques to create real-world intelligent systems.
- Apply your existing technical knowledge to design, train, and deploy advanced ML models for practical problems.
- Leverage programming skills to optimize algorithms, implement neural networks, and improve AI system performance.
Top 1% Edge
Harness cutting-edge ML techniques and hands-on projects to solve complex problems, positioning yourself as a leader in AI and data-driven innovation.
Our NBFC Partners
How is this Programme different from others?
The Future Of Machine Learning with Python Program Is Here
of enterprises say AI and ML are crucial for gaining competitive advantage in business and staying ahead in the market.
Source: Deloitte Insightsof companies will integrate AI and ML tools to optimize their decision-making processes, improving efficiency and business outcomes.
Source: Accenture"Just as electricity transformed industries, machine learning will transform our world."
About MIT IDSS
The MIT Institute for Data, Systems, and Society (IDSS) advances data science, machine learning, and societal systems through a multidisciplinary approach. It develops future data and AI leaders equipped to solve high-impact human and economic challenges.
MIT – Massachusetts Institute of Technology:
Founded in 1861, MIT is a global leader in science, engineering, and technological innovation — responsible for breakthroughs that continue to shape the modern world.
Research & Innovation Ecosystem:
IDSS drives advancements in AI, network science, data-driven policy, sustainability, healthcare analytics, and intelligent systems — fast-tracking research into real-world solutions
Rankings & Global Reputation:
MIT is ranked #1 globally in the QS World University Rankings 2026 — the 12th consecutive year — reinforcing its unmatched leadership in research and academic excellence.
Industry-Focused Learning:
IDSS programs emphasize real-world application through hands-on projects, cutting-edge tools, and collaboration with top researchers and industry experts.
How will Masai Help you?
How will Masai Help you?
Real-Time Learning Ecosystem
Experience a constantly evolving curriculum with live classes and instant academic assistance, designed to keep pace with industry advancements.
Industry Integration and Projects
Participate in masterclasses and industry mentor sessions from top institutions and companies, ensuring practical and relevant skills development.
FAQs
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