Introduction to Machine Learning
Chapter 1: What is Machine Learning?
Defining Machine Learning
Why Learn Machine Learning?
Examples of ML in Everyday Life
Distinguishing ML from Traditional Programming
Types of Machine Learning Systems
The Machine Learning Workflow Overview
Chapter 2: Fundamental Concepts
Data: The Fuel for Machine Learning
Features and Labels Explained
Training, Validation, and Test Sets
Models: Learning from Data
Parameters and Hyperparameters
Introduction to Overfitting and Underfitting
Measuring Performance: Basic Metrics
Chapter 3: Supervised Learning: Regression
Understanding Regression Problems
Introduction to Linear Regression
How Linear Regression Learns
Cost Functions: Measuring Error
Gradient Descent: Finding the Best Fit
Simple Linear Regression Example
Practice: Implementing Simple Linear Regression
Chapter 4: Supervised Learning: Classification
Understanding Classification Problems
Introduction to Logistic Regression
Introduction to K-Nearest Neighbors (KNN)
Evaluating Classification Models
Practice: Implementing KNN for Classification
Chapter 5: Unsupervised Learning: Clustering
What is Unsupervised Learning?
Introduction to Clustering
Choosing the Number of Clusters (K)
How K-Means Finds Clusters
Practice: Applying K-Means to Simple Data
Chapter 6: Preparing Your Data
The Importance of Data Preprocessing
Introduction to Feature Scaling
Encoding Categorical Features
Splitting Data into Training and Testing Sets Revisited
Hands-on Practical: Basic Data Cleaning Steps
Chapter 7: Building Your First Machine Learning Model
Recap: The ML Workflow Steps
Choosing the Right Algorithm
Loading and Preparing Data with a Library
Evaluating Model Performance
Hands-on Practical: End-to-End Simple Model Building
