AWS Certified Machine Learning Specialty MLS-C01 Certification Guide

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.

Table of Contents

1. Machine Learning Fundamentals
2. AWS Application Services for AI/ML
3. Data preparation and transformation
4. Data understanding and visualization
5. AWS services for data storing
6. AWS Services for data migration and processing
7. Machine Learning Algorithms
8. Model evaluation and optimization
9. SageMaker modeling

Somanath has 10 years of working experience in IT industry which includes Prod development, Devops, Design and architect products from end to end. He has also worked at AWS as a Big Data Engineer for about 2 years.

Weslley Moura has 17 years of working experience in Information Technology (last 9 years working on data teams and last 5 years working as a lead data scientist). He has been working in a variety of industries, such as financial, telecommunications, healthcare, and logistics. In 2019, he was a nominee for data scientist of the year by The European DatSci & AI Awards.