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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

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

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

Chapter 4: Supervised Learning: Classification

Understanding Classification Problems

Introduction to Logistic Regression

The Sigmoid Function

Decision Boundaries

Introduction to K-Nearest Neighbors (KNN)

Evaluating Classification Models

Practice: Implementing KNN for Classification

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

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

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

Preparing Your Data

Machine learning models are highly dependent on the quality of the data they are trained on. Often, the datasets you encounter will require significant preparation before they can be effectively used by algorithms. Real-world data is frequently incomplete, inconsistent, or not in the correct format for processing.

This chapter introduces fundamental data preprocessing techniques. You will learn practical methods for:

Identifying and handling missing values in your dataset.
Applying feature scaling, such as Normalization $x' = \frac{x - \min(x)}{\max(x) - \min(x)}$ and Standardization $x' = \frac{x - \mu}{\sigma}$ to bring numerical features to a common scale.
Encoding categorical (non-numeric) features into a numerical representation that models can understand, using methods like One-Hot Encoding.
Implementing the split of data into training and testing sets.

By the end of this chapter, you will understand why these steps are necessary and how to perform basic data cleaning and transformation tasks to prepare data for machine learning models.

Sections

6.1 The Importance of Data Preprocessing
6.2 Handling Missing Values
6.3 Introduction to Feature Scaling
6.4 Encoding Categorical Features
6.5 Splitting Data into Training and Testing Sets Revisited
6.6 Hands-on Practical: Basic Data Cleaning Steps

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