Practical Machine Learning Notes and Projects

A structured journey through core Machine Learning concepts, from foundational theory to practical application, culminating in an end-to-end project.

📂 Repository Structure

.
├── 01-Lesson-01/    # Introduction to ML & Linear Regression
├── 02-Lesson-02/    # Advanced Linear Models & Regularization
├── 03-Lesson-03/    # Data Preprocessing
├── 04-Lesson-04/    # Model Evaluation & Hyperparameter Tuning
├── 05-Project-01/   # Used Cars Price Prediction Project
└── README.md        # This file

🧠 Curriculum Overview

Lesson 01: Introduction to ML & Linear Regression

• Topics Covered:
  • What is Artificial Intelligence (AI) vs. Machine Learning (ML)
  • History of AI and why it's relevant now
  • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
  • Simple Linear Regression
  • Cost Function (MSE) and Gradient Descent
  • Normal Equation vs. Gradient Descent

Lesson 02: Advanced Linear Models & Regularization

• Topics Covered:
  • Multiple Linear Regression
  • Polynomial Regression
  • Bias-Variance Tradeoff
  • Overfitting and Underfitting
  • Regularization Techniques:
    • L1 Regularization (Lasso)
    • L2 Regularization (Ridge)
    • Elastic Net
  • Implementation with Scikit-Learn

Lesson 03: Data Preprocessing

• Topics Covered:
  • Handling Categorical Data (Ordinal & One-Hot Encoding)
  • Dealing with Missing Values (Imputation)
  • Feature Scaling (Normalization vs. Standardization)
  • Proper Train-Test Split methodology
  • Preventing Data Leakage

Lesson 04: Model Evaluation & Tuning

• Topics Covered:
  • Regression Evaluation Metrics (MSE, RMSE, MAE, R² Score)
  • Cross-Validation Techniques (K-Fold, LOOCV)
  • Hyperparameter Tuning:
    • Grid Search
    • Random Search
  • Understanding R² Score interpretation

Project 1: Used Cars Price Prediction

• Project Overview: An end-to-end machine learning project predicting used car prices
• Skills Demonstrated:
  • Data cleaning and preprocessing
  • Exploratory Data Analysis (EDA)
  • Feature engineering
  • Model selection and evaluation
  • Hyperparameter tuning
  • Result interpretation and reporting

This Repo is under Construction.