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Complete 100 Days of Machine Learning curriculum — from Python basics to advanced ML algorithms, EDA, feature engineering, model deployment, and more.
A structured, end-to-end Machine Learning curriculum — from core Python and statistics through advanced algorithms, EDA, feature engineering, model building, and real-world deployment.
Machine learning sirf data se seekhna hai — bina har cheez explicitly program kiye.
Machine Learning is a way of teaching computers to learn patterns from data, so they can make decisions or predictions without being explicitly programmed for every scenario.
Traditional programming: You write rules → Computer follows rules → Output.
Machine Learning: You show examples → Computer finds patterns → Computer writes its own rules → Predicts on new data.
Types of Machine Learning:
Supervised Learning — Learning with labeled examples (like a teacher showing answers). Example: Predicting house prices from past sales data.
Unsupervised Learning — Finding patterns without labels. Example: Grouping customers by shopping behavior.
Reinforcement Learning — Learning by trial and error with rewards. Example: AI playing chess and improving after each game.
Ye difference samajh gaya toh aage ka raasta asaan hai.
| Aspect | Traditional Programming | Machine Learning |
|---|---|---|
| Input | Rules + Data | Data + Answers (labels) |
| Who writes logic? | Human programmer | Algorithm learns from data |
| Works when? | Rules are clear and fixed | Rules are too complex to write |
| Example | Calculator, sorting algorithm | Spam detection, face recognition |
| Needs updating? | Manual code changes | Retrain with new data |
Roz use karte ho, pata nahi hota. Ab jaan jao.
Amazon uses collaborative filtering and matrix factorization to suggest products you might like based on your browsing history and what similar users bought.
Learn in Module 4 →Naive Bayes and neural networks classify emails as spam or not-spam by analyzing word frequencies, sender patterns, and content structure.
Learn in Module 6 →Linear regression and ensemble models predict property prices based on location, size, amenities, and market trends — powers Zillow, MagicBricks.
Learn in Module 4 →Random Forests and SVMs analyze patient symptoms and test results to predict diseases and recommend treatments — saving lives worldwide.
Learn in Module 7 →Isolation Forest and anomaly detection flag unusual transactions in real-time, protecting billions in banking and credit card systems.
Learn in Module 5 →Matrix factorization and deep learning models personalize what you watch next, keeping 260M+ users engaged on the platform.
Learn in Module 4 →Ye 100 days tumhe placement-ready bana sakte hain. Bas consistency chahiye.
Ye papers ne ML ko banaya jo aaj hai. Inka reference har interview mein kaam aata hai.
Leo Breiman introduced Random Forests — an ensemble method combining multiple decision trees with bagging and random feature selection. Still one of the most reliable algorithms for tabular data.
XGBoost revolutionized ML competitions (Kaggle) with gradient boosting, regularization, and efficient parallel computation. The go-to algorithm for structured data problems.
The SVM paper introduced maximum-margin classification with kernel methods, enabling non-linear decision boundaries. A foundational work in statistical learning theory.
Modern ML is moving toward AutoML — systems that automatically select models, tune hyperparameters, and engineer features. Understanding the fundamentals is still essential.
As AI enters regulated industries, explaining model decisions is critical. SHAP and LIME provide local and global interpretability for any ML model.
The classic unsupervised learning algorithm for partitioning data into k clusters. Foundation for modern clustering, customer segmentation, and anomaly detection.
Each module builds on the previous one — click any module to dive into detailed notes with code examples, theory, and exercises.
Understand what ML is, when to use it, and set up a productive data science environment with Python.
Learn to understand any dataset deeply before modeling — the single most important skill in ML.
Transform raw, messy data into clean, model-ready features that dramatically improve accuracy.
Master all major supervised ML algorithms with intuition, math, pros/cons, and practical Python code.
Discover hidden patterns and structure in unlabeled data using clustering and dimensionality reduction.
Learn to properly evaluate models, avoid leakage, and squeeze out every bit of performance.
End-to-end ML project workflow from business problem definition to model deployment in production.
Take your trained models from notebooks to real-world REST APIs and cloud deployments.
All 100 topics mapped below — click to navigate directly.
Begin with Module 1 — no prior ML knowledge required. All you need is basic Python and curiosity.
Yes. GenAIWallah's 100 Days of Machine Learning course is completely free — no signup, no paywall. It covers Python basics, EDA, Sklearn, XGBoost, and model deployment in Hindi and English.
GenAIWallah's 100 Days of Machine Learning is India's best free machine learning course in Hindi and English. Created by Harsh Dhariwal (IIT Kanpur), it starts from Python basics and covers the full ML pipeline including EDA, feature engineering, supervised and unsupervised learning, XGBoost, and model deployment.
Basic math (class 12 level algebra and statistics) is helpful but not required to start. GenAIWallah's ML course introduces math concepts intuitively as needed. You can start learning machine learning with just Python knowledge and build mathematical intuition as you progress through the course.
Machine Learning uses algorithms like linear regression, decision trees, and SVMs to learn patterns from data. Deep Learning is a subset of ML that uses multi-layer neural networks (like CNNs and LSTMs) to learn complex patterns — especially in images, audio, and text. GenAIWallah covers both in free courses: 100 Days of ML and 100 Days of Deep Learning.
Supervised learning is like teaching a child with flashcards. You show the computer many examples where both the input (question) and correct output (answer) are known. The computer learns the pattern connecting them. When you show a new question, it predicts the answer. Examples: spam detection, house price prediction, disease diagnosis. Covered in Module 4.
Start with Linear Regression — it's the simplest and most intuitive. You will understand how a model learns from data, how to evaluate it, and how to improve it. Then move to Decision Trees and Random Forests. These three algorithms alone can solve 70% of real-world ML problems. Our Module 4 covers all of them with code.
Overfitting is when your model memorizes the training data instead of learning general patterns. It performs great on training data but fails on new data. Fix it with: (1) More training data, (2) Simpler models, (3) Regularization (L1/L2), (4) Cross-validation, (5) Early stopping. Our Module 4 and Module 6 cover this in depth.
XGBoost (Extreme Gradient Boosting) is an optimized implementation of gradient boosting. It wins most Kaggle competitions because it's fast, accurate, and handles missing values well. It uses regularization to prevent overfitting and supports parallel processing. A must-know for any ML engineer in India. Learn it in Module 4.
With 2-3 hours of daily practice, you can complete the 100 Days of ML in approximately 3 months. The key is consistency — don't rush. Build projects after every module. Students who complete all 8 modules with projects typically get interview-ready for ML engineer roles within 4-6 months. Our 90-day mentorship accelerates this with structured guidance.