Home Blog Learn

All Courses

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

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

Handling Missing Values

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

Accessing Pipeline Steps

Using Pipelines with Cross-Validation

Grid Search with Pipelines

Constructing Complex Pipelines with ColumnTransformer

Hands-on Practical: Pipeline Construction and Tuning

Supervised Learning: Regression

This chapter introduces supervised learning for regression tasks. Regression aims to predict continuous numerical values, such as predicting house prices or temperature based on relevant features.

You will start with the fundamentals of linear regression, a foundational algorithm for this type of problem. We will cover how to:

Implement linear regression models using Scikit-learn's consistent API, specifically the LinearRegression estimator.
Interpret the coefficients learned by the model to understand feature importance.
Evaluate model performance using standard regression metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and the $R^2$ score.
Calculate these metrics effectively within Scikit-learn.

By the end of this chapter, you will build and evaluate a complete regression model using a practical dataset, applying the concepts learned.

Sections

2.1 Introduction to Regression Problems
2.2 Linear Regression Fundamentals
2.3 Implementing Linear Regression with Scikit-learn
2.4 Interpreting Model Coefficients
2.5 Regression Evaluation Metrics
2.6 Calculating Metrics in Scikit-learn
2.7 Hands-on Practical: Building a Regression Model

© 2025 ApX Machine Learning

;