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


  1. You are working as a data scientist for a fintech company. At the moment, you are working on a regression model that predicts how much money customers will spend on their credit card transactions in the next month. You believe you have created a good model; however, you want to complete your residual analysis to confirm that the model errors are randomly distributed around zero. What is the best chart for performing this residual analysis?

    a) Line chart

    b) Bubble chart

    c) Scatter plot

    d) Stacked bar chart


    C, In this case, you want to show the distribution of the model errors. A scatter plot would be a nice approach to present such an analysis. Having model errors randomly distributed across zero is just more evidence that the model is not suffering from overfitting. Histograms are also nice for performing error analysis.

  2. Although you believe that two particular variables are highly correlated, you think this is not a linear correlation. Knowing the type of correlation...