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
Logistic Regression for Classification
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- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009 (Springer) DOI: 10.1007/978-0-387-84858-7 - This classic textbook provides a comprehensive statistical foundation for logistic regression, covering the sigmoid function, maximum likelihood estimation (which leads to log loss), decision boundaries, and its role as a generalized linear model.
- Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006 (Springer) DOI: 10.1007/978-0-387-44958-3 - A highly regarded textbook that covers logistic regression from a probabilistic perspective, detailing the derivation of the likelihood function, cross-entropy error, and optimization methods.
- sklearn.linear_model.LogisticRegression, Scikit-learn developers, 2023 - Official documentation for Scikit-learn's Logistic Regression class, detailing parameters such as C, penalty, solver, as well as methods like predict_proba, and handling of multi-class problems.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - A practical guide that effectively bridges theoretical concepts of logistic regression with its implementation in Scikit-learn, including explanations of regularization and hyperparameter tuning.
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