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

Unsupervised learning

AWS provides several unsupervised learning algorithms for the following tasks:

  • Clustering:
  • K-means algorithm
  • Dimension reduction:
  • Principal Component Analysis (PCA)
  • Pattern recognition:
  • IP Insights
  • Anomaly detection:
  • Random Cut Forest Algorithm (RCF)

Let's start by talking about clustering and how the most popular clustering algorithm works: K-means.


Clustering algorithms are very popular in data science. Basically, they aim to identify groups in a given dataset. Technically, we call these findings or groups clusters. Clustering algorithms belong to the field of non-supervised learning, which means that they don't need a label or response variable to be trained.

This is just fantastic because labeled data used to be scarce. However, it comes with some limitations. The main one is that clustering algorithms provide clusters for you, but not the meaning of each cluster. Thus, someone, as a...