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

Understanding data distributions

Although the Gaussian distribution is probably the most common distribution for statistical and machine learning models, you should be aware that it is not the only one. There are other types of data distributions, such as the Bernoulli, Binomial, and Poisson distributions.

The Bernoulli distribution is a very simple one, as there are only two types of possible events: success or failure. The success event has a probability "p" of happening, while the failure one has a probability of "1-p".

Some examples that follow a Bernoulli distribution are rolling a six-sided die or flipping a coin. In both cases, you must define the event of success and the event of failure. For example, suppose our events for success and failure in the die example are as follows:

  • Success: Getting a number 6
  • Failure: Getting any other number

We can then say that we have a p probability of success (1/6 = 0.16 = 16%) and a 1-p probability...