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

Textual analysis

Modern applications use Natural Language Processing (NLP) for several purposes, such as text translation, document classifications, web search, named entity recognition (NER), and many others.

AWS offers a suite of algorithms for most NLP use cases. In the next few subsections, we will have a look at these built-in algorithms for textual analysis.

Blazing Text algorithm

Blazing Text does two different types of tasks: text classification, which is a supervised learning approach that extends the fastText text classifier, and word2vec, which is an unsupervised learning algorithm.

The Blazing Text's implementations of these two algorithms are optimized to run on large datasets. For example, you can train a model on top of billions of words in a few minutes.

This scalability aspect of Blazing Text is possible due to the following:

  • Its ability to use multi-core CPUs and a single GPU to accelerate text classification
  • Its ability to use multi...