Summary  
This chapter introduces the main machine learning paradigms—supervised learning for labeled regression and classification, unsupervised learning for clustering, anomaly detection, and dimensionality reduction without labels, and reinforcement learning based on reward signals.

General domain of usage  
Machine learning

**Supervised learning** is a machine learning technique in which the model is trained on a **labeled training set**.

Definition

The most popular supervised learning tasks are:
- **Regression** (for example, predicting the price of a house): you will need a training set labeled with other house prices for that;

- **Classification** (for example, classifying email as spam/ham): you will need a training set labeled as spam/ham for that.

**Unsupervised learning** is a machine learning technique in which the model is trained on an **unlabeled training set**.

The main unsupervised learning tasks are **clustering**, **anomaly detection**, and **dimensionality reduction**.

### Clusterization

Groups similar data points into clusters **without labels** — for example, grouping emails without knowing whether they are spam or not.

### Anomaly Detection

Finds data points that **deviate** from normal patterns, such as unusual credit card transactions, without needing fraud labels.

### Dimensionality Reduction

Reduces the number of features while preserving important information — also label-free.

## Reinforcement Learning

Reinforcement learning differs significantly from the previous two types. It is a technique used to train self-driving vehicles, robots, AI in gaming, and more.

**Reinforcement learning** is a machine learning technique in which the **agent** (e.g., vacuum cleaner robot) learns by making decisions and getting a **reward** if the decision is correct and a **penalty** if the decision is wrong.

Training a dog to fetch works similarly to reinforcement learning:
good actions earn a **reward**, wrong actions earn a **penalty**, and successfully bringing the ball earns a **larger reward**, reinforcing the desired behavior.

To train the ML model for a supervised learning task, you need a training set to contain target (be labeled). Is it correct?

To train the ML model for a unsupervised learning task, containing a target (being labeled) for a training set is not required. Is it correct?

Machine learning drives modern technological innovation across all industries. Embark on a comprehensive introduction to predictive modeling by mastering foundational algorithmic concepts utilizing Scikit-Learn. Participants will construct robust classifiers, evaluate performance metrics, ultimately culminating in the development of a complete predictive project.

Types of Machine Learning

Supervised Learning

Unsupervised Learning

Clusterization

Anomaly Detection

Dimensionality Reduction

Reinforcement Learning

1. To train the ML model for a supervised learning task, you need a training set to contain target (be labeled). Is it correct?

2. To train the ML model for a unsupervised learning task, containing a target (being labeled) for a training set is not required. Is it correct?