Machine Learning (ML)

 Artificial Intelligence (AI)

 Explore intelligent agents, planning, and reasoning across real‑world tasks.

Explore Apply

 Machine Learning (ML)

 Implement supervised/unsupervised models, feature engineering, and evaluation.

Explore Apply

Day 1 — What is ML?

Types (Supervised, Unsupervised, Reinforcement)

Day 2 — Python Libraries

NumPy, Pandas, Matplotlib, Seaborn

Day 3 — ML/DL Foundations I

Missing values, encoding, scaling

Day 4 — Train-Test Split

Train-Test Split, Cross-validation, Performance Metrics

Day 5 — Linear Regression

Theory + implementation

Day 6 — Multiple Linear Regression

Multiple Linear Regression & Polynomial Regression

Day 7 — Mini Project

House Price Prediction (Regression)

Day 8 — Logistic Regression

Logistic Regression

Day 9 — Decision Trees

Decision Trees & Random Forests

Day 10 — K-Nearest Neighbors

K-Nearest Neighbors (KNN)

Day 11 — Support Vector Machines

Support Vector Machines (SVM)

Day 12 — Naïve Bayes

Naïve Bayes Classifier

Day 13 — K-Means Clustering

K-Means Clustering (unsupervised)

Day 14 — Mini Project

Iris Flower Classification

Day 15 — Feature Selection

Feature Selection & Engineering

Day 16 — Ensemble Methods

Bagging, Boosting, AdaBoost, XGBoost

Day 17 — Dimensionality Reduction

PCA, t-SNE

Day 18 — Overfitting & Regularization

Overfitting, Underfitting, Regularization (Lasso, Ridge)

Day 19 — Hyperparameter Tuning

Grid Search, Random Search

Day 20 — Model Evaluation

Confusion Matrix, ROC, AUC, F1-score

Day 21 — Mini Project

Spam Email Classifier

Day 22 — Time Series Forecasting

Basics, ARIMA intro

Day 23 — Recommender Systems

Content-based, Collaborative Filtering

Day 24 — Natural Language Processing

Bag-of-Words, TF-IDF

Day 25 — Sentiment Analysis

NLP + ML

Day 26 — ML Pipelines

ML Pipelines & Deployment Basics

Day 27 — Final Project Selection

Choose one: Customer Churn Prediction / Movie Recommendation System / Fake News Detection / Stock Price Forecasting

Day 28 — Final Project Development

Data collection & preprocessing

Day 29 — Final Project Implementation

Model building & evaluation

Day 30 — Project Presentation

Project Presentation + GitHub Upload + Wrap-Up