Machine Learning Basics | Chapter 2: Types of Machine Learning by Prakhar Chauhan | Learn Smarter
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Chapter 2: Types of Machine Learning

The chapter introduces the three primary types of machine learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. It provides definitions and real-life analogies for each type, explain how machines learn based on examples, and includes simple Python code examples for better understanding. The chapter emphasizes the importance of these learning types in making decisions based on data.

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Sections

  • 2

    Types Of Machine Learning

    This section introduces the three main types of machine learning: supervised, unsupervised, and reinforcement learning, along with their applications and examples.

    Learning Practice

  • 2.1

    Why Different Types Of Learning?

    This section explains the different types of learning in machine learning: supervised, unsupervised, and reinforcement learning.

    Learning Practice

  • 2.2

    Supervised Learning — Learning With Answers

    Supervised learning involves machines learning from labeled data to make predictions or classifications.

    Learning Practice

  • 2.2.1

    What Is It?

    This section introduces Supervised Learning, a type of machine learning where a computer learns from examples with correct answers.

    Learning Practice

  • 2.2.2

    Real-Life Analogy

    Real-life analogies help illustrate the concept of supervised learning in machine learning.

    Learning Practice

  • 2.2.3

    Tasks Where It’s Used

    This section outlines real-world applications of supervised learning techniques in machine learning.

    Learning Practice

  • 2.2.4

    Two Subtypes Of Supervised Learning

    This section introduces the two primary subtypes of supervised learning: regression and classification.

    Learning Practice

  • 2.2.4.1

    Regression — Output Is A Number

    This section focuses on regression, a subtype of supervised learning in machine learning, where the output is a continuous numerical value based on input data.

    Learning Practice

  • 2.2.4.2

    Classification — Output Is A Category

    Classification is a subtype of supervised learning where the machine predicts categorical outcomes based on input data.

    Learning Practice

  • 2.2.5

    Example 1: Regression (Predict Numbers)

    Learning

  • 2.2.6

    Example 2: Classification (Predict Categories)

    Learning

  • 2.3

    Unsupervised Learning — Learning Without Answers

    Unsupervised Learning involves machines analyzing data without pre-existing labels or answers to identify patterns and structures.

    Learning Practice

  • 2.3.1

    What Is It?

    This section introduces the three main types of machine learning: supervised, unsupervised, and reinforcement learning.

    Learning Practice

  • 2.3.2

    What Can It Do?

    This section discusses the capabilities of unsupervised learning in machine learning, highlighting its functionalities, such as grouping, finding patterns, and detecting anomalies.

    Learning Practice

  • 2.3.3

    Example: Clustering Customers

    Learning

  • 2.4

    Reinforcement Learning — Learning By Trial & Reward

    Reinforcement Learning allows machines to learn by taking actions and receiving rewards or penalties, similar to how animals learn through trial and error.

    Learning Practice

  • 2.4.1

    What Is It?

    This section introduces the three primary types of machine learning: supervised, unsupervised, and reinforcement learning.

    Learning Practice

  • 2.4.2

    Real Examples

    This section discusses real-world applications of different machine learning techniques.

    Learning Practice

  • 2.4.3

    Feedback Loop

    The feedback loop is a key concept in Reinforcement Learning, whereby an agent learns from actions taken and the consequences that follow, refining its strategies over time.

    Learning Practice

  • 2.4.4

    Note

    This section discusses the importance of different types of machine learning and introduces the concepts of supervised, unsupervised, and reinforcement learning.

    Learning Practice

  • 2.5

    Summary Table

    This section summarizes the main types of machine learning and their characteristics.

    Learning Practice

  • 2.6

    Final Thoughts For Beginners

    The section encourages beginners to start with supervised learning and explore more complex methods gradually.

    Learning Practice

References

Untitled document (34).pdf

Class Notes

Memorization

What we have learnt

  • Machine learning is divided...
  • Supervised learning utilize...
  • Unsupervised learning ident...

Final Test

Revision Tests