Getting Started with Scikit-Learn
Chapter 1: Introduction to Scikit-learn and Setup
What is Scikit-learn?
Installation and Environment Setup
Overview of the Scikit-learn API
Data Representation in Scikit-learn
Loading Example Datasets
Hands-on Practical: Setup Verification
Quiz for Chapter 1
Chapter 2: Supervised Learning: Regression
Introduction to Regression Problems
Linear Regression Fundamentals
Implementing Linear Regression with Scikit-learn
Interpreting Model Coefficients
Regression Evaluation Metrics
Calculating Metrics in Scikit-learn
Hands-on Practical: Building a Regression Model
Quiz for Chapter 2
Chapter 3: Supervised Learning: Classification
Introduction to Classification Problems
Logistic Regression for Classification
K-Nearest Neighbors (KNN) Algorithm
Implementing KNN with Scikit-learn
Support Vector Machines (SVM) Basics
Implementing SVM with Scikit-learn
Classification Evaluation Metrics
Calculating Metrics in Scikit-learn
Hands-on Practical: Building Classification Models
Quiz for Chapter 3
Chapter 4: Data Preprocessing and Feature Engineering
The Importance of Data Preprocessing
Feature Scaling Techniques
Applying Scalers in Scikit-learn
Encoding Categorical Features
Applying Encoders in Scikit-learn
Handling Missing Values
Using Imputers in Scikit-learn
Hands-on Practical: Preprocessing Data
Quiz for Chapter 4
Chapter 5: Model Selection and Evaluation
The Problem of Overfitting and Underfitting
Splitting Data: Training and Testing Sets
Introduction to Cross-Validation
Implementing K-Fold Cross-Validation
Stratified K-Fold for Classification
Grid Search for Hyperparameter Tuning
Hands-on Practical: Model Evaluation and Selection
Quiz for Chapter 5
Chapter 6: Building Pipelines
Motivation for Using Pipelines
Creating a Simple Pipeline
Accessing Pipeline Steps
Using Pipelines with Cross-Validation
Grid Search with Pipelines
Constructing Complex Pipelines with ColumnTransformer
Hands-on Practical: Pipeline Construction and Tuning
Quiz for Chapter 6
The Problem of Overfitting and Underfitting
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- An Introduction to Statistical Learning with Applications in Python, Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, Jonathan Taylor, 2023 (Springer) - Provides an accessible introduction to machine learning concepts, including detailed explanations of underfitting, overfitting, and the bias-variance tradeoff.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - Presents a rigorous treatment of statistical learning methods, offering comprehensive coverage of the bias-variance decomposition.
- CS229 Lecture Notes: Supervised Learning, Generative Learning Algorithms, Andrew Ng, 2018 (Stanford University) - These lecture notes from a widely recognized machine learning course cover fundamental concepts such as bias, variance, and model selection.
- Cross-validation: evaluating estimator performance, scikit-learn developers, 2024 - Official documentation explaining how to use cross-validation in scikit-learn to assess model performance and avoid overfitting.
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