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
Tools You Might Use
Quiz for Chapter 1
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
Quiz for Chapter 2
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
Quiz for Chapter 3
Chapter 4: Supervised Learning: Classification
Understanding Classification Problems
Introduction to Logistic Regression
The Sigmoid Function
Decision Boundaries
Introduction to K-Nearest Neighbors (KNN)
How KNN Works
Evaluating Classification Models
Practice: Implementing KNN for Classification
Quiz for Chapter 4
Chapter 5: Unsupervised Learning: Clustering
What is Unsupervised Learning?
Introduction to Clustering
The K-Means Algorithm
Choosing the Number of Clusters (K)
How K-Means Finds Clusters
Limitations of K-Means
Practice: Applying K-Means to Simple Data
Quiz for Chapter 5
Chapter 6: Preparing Your Data
The Importance of Data Preprocessing
Handling Missing Values
Introduction to Feature Scaling
Encoding Categorical Features
Splitting Data into Training and Testing Sets Revisited
Hands-on Practical: Basic Data Cleaning Steps
Quiz for Chapter 6
Chapter 7: Building Your First Machine Learning Model
Recap: The ML Workflow Steps
Choosing the Right Algorithm
Loading and Preparing Data with a Library
Training a Simple Model
Making Predictions
Evaluating Model Performance
Hands-on Practical: End-to-End Simple Model Building
Quiz for Chapter 7
Limitations of K-Means
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- An Introduction to Statistical Learning: With Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, 2021 (Springer) - Provides a clear explanation of K-Means, its algorithm, and limitations, including initialization issues and assumptions about cluster shapes.
- Pattern Recognition and Machine Learning, Christopher Bishop, 2006 (Springer) DOI: 10.1007/b139369 - Offers a detailed treatment of K-Means, including its objective function and discussions on its sensitivity to initialization and geometric assumptions.
- k-means++: The Advantages of Careful Seeding, David Arthur, Sergei Vassilvitskii, 2007 Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (Society for Industrial and Applied Mathematics) DOI: 10.1145/1283383.1283494 - Introduces a widely adopted initialization algorithm, k-means++, which significantly improves the quality and consistency of K-Means results by addressing its sensitivity to initial centroid placement.