Machine Learning with Python: from Linear Models to Deep Learning
Machine Learning with Python: from Linear Models to Deep Learning
- Learn from MIT's Faculty - Ranked #1 in the world (QS 2026)
- India’s 1st collaboration with MIT IDSS
- Build a real business case with a hands-on Capstone Project
Enter the following details to continue
Batch Starts
March 18, 2026
Qualifier test date
February 15, 2026
Course Duration
7 months
Time Commitment
8-10 Hours/week
Eligibility
12th and above
Learning Mode
Online
Batch Starts
March 18, 2026
Qualifier test date
February 15, 2026
Course Duration
7 months
Time Commitment
8-10 Hours/week
Eligibility
12th and above
Learning Mode
Online
Enter the following details to continue
Admission Process
Clear Qualifier Test
Clear the qualifier test to be eligible for the program
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.
Topics Covered
- • 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
- • The duration of the test is 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)
Option 1
Upfront
Option 2
EMI
(Through Our NBFC Partners)
Secure Seat Fee
(non-refundable)
₹4,000
₹4,000
Programme Fee
(non-refundable)
₹56,000
₹7,156 x 9 months
Total
₹60,000
( + GST **)
₹68,404
( + GST **)
18% GST extra, as applicable
Our NBFC Partners
Why choose this Programme?
Become Future-Ready in Machine Learning with Industry-Aligned Curriculum and Hands-On Projects
Prestigious Certification
Receive a Certificate of Completion from edX , validating your expertise and boosting your professional credibility.
Future-Proof Career Gateway
Advanced Curriculum
Case-Based Learning
World-Class Faculty
Hands-On Projects
Prestigious Certification
Receive a Certificate of Completion from edX , validating your expertise and boosting your professional credibility.
Future-Proof Career Gateway
Launch a high-growth, future-ready career in Machine Learning with in-demand skills sought by top tech companies.
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.
Hands-On Projects
Build end-to-end ML systems like digit recognition, NLP models, and recommendation engines to demonstrate your applied expertise.
What You will 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
FOUNDATION PROGRAM
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
MIT MICROMASTERS PROGRAM (4 Modules)
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
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.
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.
Contact Us
Get started
Join the Program
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