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)
1
Section 1: Introduction to Machine Learning
4
Section 2: Data Engineering and Exploratory Data Analysis
9
Section 3: Data Modeling

Summary

We are now heading to the end of this chapter, where we have covered several important topics about the foundations of ML. We started the chapter with a theoretical discussion about AI, ML, and DL and how this entire field has grown over the past few years due to the advent of big data platforms and cloud providers.

We then moved on to the differences between supervised, unsupervised, and reinforcement learning, highlighting some use cases related to each of them. This is likely to be a topic in the AWS Machine Learning Specialty exam.

We discussed that an ML model is built in many different stages and the algorithm itself is just one part of the modeling process. We also covered the expected behaviors of a good model.

We did a deep dive into data splitting, where we talked about different approaches to train and validate models, and we covered the mythic battle between variance and bias. We completed the chapter by talking about ML frameworks and services.

Coming up next, you will learn about AWS application services for ML, such as Amazon Polly, Amazon Rekognition, Amazon Transcribe, and many other AI-related AWS services. But first, let's look into some sample questions to give you an idea of what you could expect in the exam.