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
Parameters and Hyperparameters
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- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - A classic textbook covering the statistical foundations of machine learning, including detailed explanations of model parameters in various algorithms like linear regression.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive resource for deep learning, explicitly detailing parameters (weights, biases) and hyperparameters (learning rate, network architecture) within neural networks.
- CS229 Lecture Notes: Machine Learning, Andrew Ng, Tengyu Ma, 2023 (Stanford University) - Lecture notes from a renowned machine learning course, offering clear explanations of fundamental concepts like parameters in linear regression and hyperparameters such as the learning rate.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - A practical guide to machine learning that clearly distinguishes between parameters and hyperparameters with examples across various algorithms like K-Nearest Neighbors and neural networks.