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CampusX · 100 Days of ML

Master Machine Learning
in 100 Days

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.

100 Days / Lessons
8 Core Phases
200+ Code Examples
∞ Free Forever
Curriculum Overview

8 Modules to ML Mastery

Each module builds on the previous one — click any module to dive into detailed notes with code examples, theory, and exercises.

Module 1
10

Foundations & Python Essentials

Understand what ML is, when to use it, and set up a productive data science environment with Python.

  • What is Machine Learning? AI vs ML vs DL
  • ML Life Cycle & Product Development Flow
  • Python for Data Science (NumPy, Pandas)
  • Jupyter Notebooks & Development Setup
  • Types of ML: Supervised, Unsupervised, Reinforcement
  • Batch vs Online Learning, Instance vs Model-Based
Start Module 1 →
Module 2
25

Exploratory Data Analysis (EDA)

Learn to understand any dataset deeply before modeling — the single most important skill in ML.

  • Univariate Analysis — Histograms, Box Plots, KDE
  • Bivariate Analysis — Scatter Plots, Correlation Heatmaps
  • Multivariate Analysis with Seaborn & Matplotlib
  • Handling Missing Values & Outlier Detection
  • Pandas Profiling & Automated EDA
  • Case Study: Titanic Survival Analysis
Start Module 2 →
Module 3
40

Data Preprocessing & Feature Engineering

Transform raw, messy data into clean, model-ready features that dramatically improve accuracy.

  • Handling Missing Data: Imputation Strategies
  • Encoding Categorical Variables (OHE, Label, Target)
  • Feature Scaling: StandardScaler, MinMaxScaler, RobustScaler
  • Feature Transformation: Log, Box-Cox, Power Transforms
  • Feature Selection: Filter, Wrapper, Embedded Methods
  • Handling Imbalanced Datasets: SMOTE, Class Weights
  • Pipelines with Scikit-Learn ColumnTransformer
Start Module 3 →
Module 4
60

Supervised Learning Algorithms

Master all major supervised ML algorithms with intuition, math, pros/cons, and practical Python code.

  • Linear & Logistic Regression — Gradient Descent
  • Decision Trees — Gini Impurity, Entropy, ID3/CART
  • Support Vector Machines (SVM) — Kernels & Margins
  • Naive Bayes — Gaussian, Multinomial, Bernoulli
  • K-Nearest Neighbors (KNN) — Distance Metrics
  • Random Forests & Extra Trees
  • Gradient Boosting: XGBoost, LightGBM, CatBoost
Start Module 4 →
Module 5
70

Unsupervised Learning

Discover hidden patterns and structure in unlabeled data using clustering and dimensionality reduction.

  • K-Means Clustering — Inertia, Elbow Method, K-Means++
  • Hierarchical Clustering — Dendrograms, Linkage
  • DBSCAN — Density-Based Clustering
  • Principal Component Analysis (PCA)
  • t-SNE & UMAP for Visualization
  • Anomaly Detection — Isolation Forest, LOF
Start Module 5 →
Module 6
80

Model Evaluation & Hyperparameter Tuning

Learn to properly evaluate models, avoid leakage, and squeeze out every bit of performance.

  • Train/Validation/Test Split — Data Leakage Risks
  • Cross-Validation: K-Fold, Stratified, Time-Series
  • Classification Metrics: Precision, Recall, F1, AUC-ROC
  • Regression Metrics: MAE, RMSE, R², MAPE
  • Bias-Variance Tradeoff — Learning Curves
  • GridSearchCV, RandomizedSearchCV, Optuna
  • Ensemble Methods: Bagging, Boosting, Stacking
Start Module 6 →
Module 7
90

Machine Learning Project Life Cycle

End-to-end ML project workflow from business problem definition to model deployment in production.

  • Problem Definition & Success Metrics
  • Data Collection Strategies
  • Complete EDA-to-Model Pipeline
  • Model Selection Framework
  • Experiment Tracking with MLflow
  • Case Studies: Real ML Projects
Start Module 7 →
Module 8
100

ML Deployment & Production

Take your trained models from notebooks to real-world REST APIs and cloud deployments.

  • Model Serialization: Pickle, Joblib, ONNX
  • Building REST APIs with Flask & FastAPI
  • Docker Containerization for ML Models
  • Deploying to Heroku / AWS / GCP
  • CI/CD for Machine Learning Pipelines
  • Model Monitoring & Drift Detection
Start Module 8 →
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All 100 topics mapped below — click to navigate directly.

What is ML? AI vs ML vs DL Types of ML Batch vs Online Python Setup NumPy Basics Pandas Basics Pandas Advanced ML Life Cycle Sklearn Intro Univariate EDA Bivariate EDA Missing Data Outlier Detection Matplotlib Seaborn Plotly Correlation Pandas Profiling EDA Case Study Bivariate+Seaborn Multivariate Feature Insights Titanic EDA EDA Project Missing Data Imputation SimpleImputer KNN Imputer Feature Scaling Encoding Categoricals OHE & Label Encoding Target Encoding Feature Transforms Pipelines ColumnTransformer Class Imbalance SMOTE Feature Selection Wrapper Methods Embedded Methods Linear Regression Gradient Descent Ridge & Lasso Logistic Regression Decision Trees Gini vs Entropy KNN Algorithm Naive Bayes SVM Theory SVM Kernels Random Forests Bagging AdaBoost Gradient Boosting XGBoost LightGBM CatBoost Stacking Voting Classifier Algorithm Comparison K-Means Elbow Method Hierarchical Clustering DBSCAN PCA t-SNE UMAP Anomaly Detection Association Rules Unsupervised Projects Train/Test Split Cross-Validation Confusion Matrix Precision & Recall ROC-AUC Curve Regression Metrics Bias-Variance GridSearchCV Optuna Ensemble Methods ML Life Cycle Problem Framing Data Collection End-to-End Project MLflow Tracking Case Study 1 Case Study 2 Interview Prep Portfolio Building Mid-Review Flask API FastAPI Streamlit Apps Docker Basics Docker for ML Cloud Deployment Model Monitoring CI/CD Pipelines Capstone Project Final Review 🎓

Ready to Start Your ML Journey?

Begin with Module 1 — no prior ML knowledge required. All you need is basic Python and curiosity.

Start Day 1 → LangChain Tutorial All Generative AI Tutorials →

Frequently Asked Questions

Is this machine learning course free?

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.

Best machine learning course in Hindi?

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.

Do I need math to learn machine learning?

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.

What is the difference between machine learning and deep learning?

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.