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

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