Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By : Somanath Nanda, Weslley Moura
Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By: Somanath Nanda, Weslley Moura

Overview of this book

The AWS Certified Machine Learning Specialty exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus using practical examples to help you with your real-world machine learning projects on AWS. Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover a wide variety of techniques for data manipulation and transformation for different types of variables. The book also shows you how to handle missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with the specific ML algorithms you need to know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them. By the end of this book, you'll have gained knowledge of the key challenges in machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS ML.
Table of Contents (14 chapters)
Section 1: Introduction to Machine Learning
Section 2: Data Engineering and Exploratory Data Analysis
Section 3: Data Modeling

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
  • Rule-based systems
  • ML

The area we are particularly interested in now is ML.

Examining ML

ML is a sub-area of AI that aims to create systems and machines that are able to learn from experience, without being explicitly programmed. As the name suggests, the system is able to observe its running environment, learn, and adapt itself without human intervention. Algorithms behind ML systems usually extract and improve knowledge from the data that is available to them, as well as conditions (such as hyperparameters), and feed back after trying different approaches to solve a particular problem:

Figure 1.1 – Heirarchy of AI, ML, DL

Figure 1.1 – Heirarchy of AI, ML, DL

There are different types of ML algorithms; for instance, we can list decision tree-based, probabilistic-based, and neural networks. Each of these classes might have dozens of specific algorithms. Most of them will be covered in later sections of this book.

As you might have noticed in Figure 1.1, we can be even more specific and break the ML field down into another very important topic for the Machine Learning Specialty exam: DL.

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 DL.

Now that we have an overview of types of AI, let's look at some of the ways we can classify ML.