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

Introducing this chapter

During this chapter, we will talk about several algorithms, modeling concepts, and learning strategies. We think all these topics will be beneficial for you during the exam and your data scientist career.

We have structured this chapter in a way so that it covers not only the necessary topics of the exam but also gives you a good sense of the most important learning strategies out there. For example, the exam will check your knowledge regarding the basic concepts of K-means; however, we will cover it on a much deeper level, since this is an important topic for your career as a data scientist.

We will follow this approach, looking deeper into the logic of the algorithm, for some types of models that we feel every data scientist should master. So, keep that in mind: sometimes, we might go deeper than expected in the exam, but that will be extremely important for you.

Many times, during this chapter, we will use the term built-in algorithms. We will use...