Getting Started with Scikit-Learn
Chapter 1: Introduction to Scikit-learn and Setup
Installation and Environment Setup
Overview of the Scikit-learn API
Data Representation in Scikit-learn
Hands-on Practical: Setup Verification
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
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
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
Using Imputers in Scikit-learn
Hands-on Practical: Preprocessing Data
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
Chapter 6: Building Pipelines
Motivation for Using Pipelines
Creating a Simple Pipeline
Using Pipelines with Cross-Validation
Grid Search with Pipelines
Constructing Complex Pipelines with ColumnTransformer
Hands-on Practical: Pipeline Construction and Tuning
