AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

The definitive guide to passing the MLS-C01 exam on the very first attempt

Somanath Nanda, Weslley Moura

• Editorial: Packt Publishing
• Año: 2021
• ISBN:
  9781800568433
• Categoría: Informatik
• Categoría: Künstliche Intelligenz (KI) & Semantik

Prepare to achieve AWS Machine Learning Specialty certification with this complete, up-to-date guide and take the exam with confidence

Key Features

• Get to grips with core machine learning algorithms along with AWS implementation
• Build model training and inference pipelines and deploy machine learning models to the Amazon Web Services (AWS) cloud
• Learn all about the AWS services available for machine learning in order to pass the MLS-C01 exam

Book Description

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.

What you will learn

• Understand all four domains covered in the exam, along with types of questions, exam duration, and scoring
• Become well-versed with machine learning terminologies, methodologies, frameworks, and the different AWS services for machine learning
• Get to grips with data preparation and using AWS services for batch and real-time data processing
• Explore the built-in machine learning algorithms in AWS and build and deploy your own models
• Evaluate machine learning models and tune hyperparameters
• Deploy machine learning models with the AWS infrastructure

Who this book is for

This AWS book is for professionals and students who want to prepare for and pass the AWS Certified Machine Learning Specialty exam or gain deeper knowledge of machine learning with a special focus on AWS. Beginner-level knowledge of machine learning and AWS services is necessary before getting started with this book.

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Section 1: Introduction to Machine Learning

This section provides details about the AWS Machine Learning Specialty Exam. It also introduces machine learning fundamentals and covers the most important AWS application services for artificial intelligence.

This section contains the following chapters:

• Chapter 1, Machine Learning Fundamentals
• Chapter 2, AWS Application Services for AI/ML

Chapter 1: Machine Learning Fundamentals

For many decades, researchers have been trying to simulate human brain activity through the field known as artificial intelligence, AI for short. In 1956, a group of people met at the Dartmouth Summer Research Project on Artificial Intelligence, an event that is widely accepted as the first group discussion about AI as we know it today. Researchers were trying to prove that many aspects of the learning process could be precisely described and, therefore, automated and replicated by a machine. Today, we know they were right!

Many other terms appeared in this field, such as machine learning (ML) and deep learning (DL). These sub-areas of AI have also been evolving for many decades (granted, nothing here is new to the science). However, with the natural advance of the information society and, more recently, the advent of big data platforms, AI applications have been reborn with much more power and applicability. Power, because now we have more computational resources to simulate and implement them; applicability, because now information is everywhere.

Even more recently, cloud services providers have put AI in the cloud. This is helping all sizes of companies to either reduce their operational costs or even letting them sample AI applications (considering that it could be too costly for a small company to maintain its own data center).

That brings us to the goal of this chapter: being able to describe what the terms AI, ML, and DL mean, as well as understanding all the nuances of an ML pipeline. Avoiding confusion on these terms and knowing what exactly an ML pipeline is will allow you to properly select your services, develop your applications, and master the AWS Machine Learning Specialty exam.

The main topics of this chapter are as follows:

• Comparing AI, ML, and DL
• Classifying supervised, unsupervised, and reinforcement learning
• The CRISP-DM modeling life cycle
• Data splitting
• Modeling expectations
• Introducing ML frameworks
• ML in the cloud

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

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.

Classifying supervised, unsupervised, and reinforcement learning

ML is a very extensive field of study; that's why it is very important to have a clear definition of its sub-divisions. From a very broad perspective, we can split ML algorithms into two main classes: supervised learning and unsupervised learning.

Introducing supervised learning

Supervised algorithms use a class or label (from the input data) as support to find and validate the optimal solution. In Figure 1.2, there is a dataset that aims to classify fraudulent transactions from a bank:

Figure 1.2 – Sample dataset for supervised learning

The first four columns are known as features, or independent variables, and they can be used by a supervised algorithm to find fraudulent patterns. For example, by combining those four features (day of the week, EST hour, transaction amount, and merchant type) and six observations (each row is technically one observation), you can infer that e-commerce transactions with a value greater than $5,000 and processed at night are potentially fraudulent cases.

Important note

In a real scenario, we should have more observations in order to have statistical support to make this type of inference.

The key point is that we were able to infer a potential fraudulent pattern just because we knew, a priori, what is fraud and what is not fraud. This information is present in the last column of Figure 1.2 and is commonly referred to as a target variable, label, response variable, or dependent variable. If the input dataset has a target variable, you should be able to apply supervised learning.

In supervised learning, the target variable might store different types of data. For instance, it could be a binary column (yes or no), a multi-class column (class A, B, or C), or even a numerical column (any real number, such as a transaction amount). According to the data type of the target variable, you will find which type of supervised learning your problem refers to. Figure 1.3 shows how to classify supervised learning into two main groups: classification and regression algorithms:

Figure 1.3 – Choosing the right type of supervised learning given the target variable

While classification algorithms predict a class (either binary or multiple classes), regression algorithms predict a real number (either continuous or discrete).

Understanding data types is important to make the right decisions on ML projects. We can split data types into two main categories: numerical and categorical data. Numerical data can then be split into continuous or discrete subclasses, while categorical data might refer to ordinal or nominal data:

• Numerical/discrete data refers to individual and countable items (for example, the number of students in a classroom or the number of items in an online shopping cart).
• Numerical/continuous data refers to an infinite number of possible measurements and they often carry decimal points (for example, temperature).
• Categorical/nominal data refers to labeled variables with no quantitative value (for example, name or gender).
• Categorical/ordinal data adds the sense of order to a labeled variable (for example, education level or employee title level).

In other words, when choosing an algorithm for your project, you should ask yourself:...

Índice

• AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide
• Contributors
• Preface
• Section 1: Introduction to Machine Learning
• Chapter 1: Machine Learning Fundamentals
• Chapter 2: AWS Application Services for AI/ML
• Section 2: Data Engineering and Exploratory Data Analysis
• Chapter 3: Data Preparation and Transformation
• Chapter 4: Understanding and Visualizing Data
• Chapter 5: AWS Services for Data Storing
• Chapter 6: AWS Services for Data Processing
• Section 3: Data Modeling
• Chapter 7: Applying Machine Learning Algorithms
• Chapter 8: Evaluating and Optimizing Models
• Chapter 9: Amazon SageMaker Modeling
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