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

That was such a journey! Let's take a moment to highlight what we have just learned. We broke this chapter into four main sections: supervised learning, unsupervised learning, textual analysis, and image processing. Everything that we have learned fits those subfields of machine learning.

The list of supervised learning algorithms that we have studied includes the following:

  • Linear learner algorithm
  • Factorization machines algorithm
  • XGBoost algorithm
  • K-Nearest Neighbors algorithm
  • Object2Vec algorithm
  • DeepAR forecasting algorithm

Remember that you can use linear learner, factorization machines, XGBoost, and KNN for multiple purposes, including to solve regression and classification problems. Linear learner is probably the simplest algorithm out of these four; factorization machines extend linear learner and are good for sparse datasets, XGBoost uses an ensemble method based on decision trees, and KNN is an index-based algorithm.

The...