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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition

You're reading from  AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition

Product type Book
Published in Feb 2024
Publisher Packt
ISBN-13 9781835082201
Pages 342 pages
Edition 2nd Edition
Languages
Authors (2):
Somanath Nanda Somanath Nanda
Profile icon Somanath Nanda
Weslley Moura Weslley Moura
Profile icon Weslley Moura
View More author details

Table of Contents (13) Chapters

Preface 1. Chapter 1: Machine Learning Fundamentals 2. Chapter 2: AWS Services for Data Storage 3. Chapter 3: AWS Services for Data Migration and Processing 4. Chapter 4: Data Preparation and Transformation 5. Chapter 5: Data Understanding and Visualization 6. Chapter 6: Applying Machine Learning Algorithms 7. Chapter 7: Evaluating and Optimizing Models 8. Chapter 8: AWS Application Services for AI/ML 9. Chapter 9: Amazon SageMaker Modeling 10. Chapter 10: Model Deployment 11. Chapter 11: Accessing the Online Practice Resources 12. Other Books You May Enjoy

Comparing AI, ML, and DL

AI is a broad field that studies different ways to create systems and machines that will solve problems by simulating human intelligence. There are different levels of sophistication to create these programs and machines, which go from simple rule-based engines to complex self-learning systems. AI covers, but is not limited to, the following sub-areas:

  • Robotics
  • Natural language processing (NLP)
  • Rule-based systems
  • Machine learning (ML)
  • Computer vision

The area this certification exam focuses on is ML.

Examining ML

ML is a sub-area of AI that aims to create systems and machines that can learn from experience, without being explicitly programmed. As the name suggests, the system can observe its underlying environment, learn, and adapt itself without human intervention. Algorithms behind ML systems usually extract and improve knowledge from the data and conditions that are available to them.

Figure 1.2 – Hierarchy of AI, ML, and DL

Figure 1.2 – Hierarchy of AI, ML, and DL

You should keep in mind that there are different classes of ML algorithms. For example, decision tree-based models, probabilistic-based models, and neural network models. Each of these classes might contain dozens of specific algorithms or architectures (some of them will be covered in later sections of this book).

As you might have noticed in Figure 1.2, you can be even more specific and break the ML field down into another very important topic for the Machine Learning Specialty exam: deep learning, or DL for short.

Examining DL

DL is a subset of ML that aims to propose algorithms that connect multiple layers to solve a particular problem. The knowledge is then passed through, layer by layer, until the optimal solution is found. The most common type of DL algorithm is deep neural networks.

At the time of writing this book, DL is a very hot topic in the field of ML. Most of the current state-of-the-art algorithms for machine translation, image captioning, and computer vision were proposed in the past few years and are a part of the DL field (GPT-4, used by the ChatGPT application, is one of these algorithms).

Now that you have an overview of types of AI, take a look at some of the ways you can classify ML.

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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition
Published in: Feb 2024 Publisher: Packt ISBN-13: 9781835082201
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