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

Dealing with numerical features

In terms of numerical features (discrete and continuous), we can think of transformations that rely on the training data and others that rely purely on the observation being transformed.

Those that rely on the training data will use the train set to learn the necessary parameters during fit, and then use them to transform any test or new data. The logic is pretty much the same as what we just reviewed for categorical features; however, this time, the encoder will learn different parameters.

On the other hand, those that rely purely on observations do not care about train or test sets. They will simply perform a mathematical computation on top of an individual value. For example, we could apply an exponential transformation to a particular variable by squaring its value. There is no dependency on learned parameters from anywhere – just get the value and square it.

At this point, you might be thinking about dozens of available transformations...