AWS certified machine learning specialty, MLS-C01 certification guide : the definitive guide to passing the MLS-C01 exam on the very first attempt / Somanath Nanda and Weslley Moura.

Format:

Book

Author/Creator:

Nanda, Somanath, author.

Moura, Weslley, author.

Language:

English

Subjects (All):

Cloud computing--Examination--Study guides.

Cloud computing.

Web services--Examinations--Study guides.

Web services.

Physical Description:

1 online resource (338 pages) : illustrations

Place of Publication:

London, England : Packt Publishing, Limited, [2021]

Biography/History:

Nanda Somanath: Somanath has 10 years of working experience in IT industry which includes Prod development, Devops, Design and architect products from end to end. He has also worked at AWS as a Big Data Engineer for about 2 years. Moura Weslley: Weslley Moura has been developing data products for the past decade. At his recent roles, he has been influencing data strategy and leading data teams into the urban logistics and blockchain industries.

Summary:

The AWS Certified Machine Learning Specialty 2020 Certification Guide covers everything you need to pass the MLS-C01 certification exam and serves as a handy, on-the-job reference guide. You'll also find the book useful if you're looking to get up to speed with AWS services for machine learning.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Table of Contents

Preface

Section 1: Introduction to Machine Learning

Chapter 1: Machine Learning Fundamentals

Comparing AI, ML, and DL

Examining ML

Examining DL

Classifying supervised, unsupervised, and reinforcement learning

Introducing supervised learning

The CRISP-DM modeling life cycle

Data splitting

Overfitting and underfitting

Applying cross-validation and measuring overfitting

Bootstrapping methods

The variance versus bias trade-off

Shuffling your training set

Modeling expectations

Introducing ML frameworks

ML in the cloud

Summary

Questions

Chapter 2: AWS Application Services for AI/ML

Technical requirements

Analyzing images and videos with Amazon Rekognition

Exploring the benefits of Amazon Rekognition

Getting hands-on with Amazon Rekognition

Text to speech with Amazon Polly

Exploring the benefits of Amazon Polly

Getting hands-on with Amazon Polly

Speech to text with Amazon Transcribe

Exploring the benefits of Amazon Transcribe

Getting hands-on with Amazon Transcribe

Implementing natural language processing with Amazon Comprehend

Exploring the benefits of Amazon Comprehend

Getting hands-on with Amazon Comprehend

Translating documents with Amazon Translate

Exploring the benefits of Amazon Translate

Getting hands-on with Amazon Translate

Extracting text from documents with Amazon Textract

Exploring the benefits of Amazon Textract

Getting hands-on with Amazon Textract

Creating chatbots on Amazon Lex

Exploring the benefits of Amazon Lex

Getting hands-on with Amazon Lex

Answers

Section 2: Data Engineering and Exploratory Data Analysis

Chapter 3: Data Preparation and Transformation

Identifying types of features.

Dealing with categorical features

Transforming nominal features

Applying binary encoding

Transforming ordinal features

Avoiding confusion in our train and test datasets

Dealing with numerical features

Data normalization

Data standardization

Applying binning and discretization

Applying other types of numerical transformations

Understanding data distributions

Handling missing values

Dealing with outliers

Dealing with unbalanced datasets

Dealing with text data

Bag of words

TF-IDF

Word embedding

Chapter 4: Understanding and Visualizing Data

Visualizing relationships in your data

Visualizing comparisons in your data

Visualizing distributions in your data

Visualizing compositions in your data

Building key performance indicators

Introducing Quick Sight

Chapter 5: AWS Services for Data Storing

Storing data on Amazon S3

Creating buckets to hold data

Distinguishing between object tags and object metadata

Controlling access to buckets and objects on Amazon S3

S3 bucket policy

Protecting data on Amazon S3

Applying bucket versioning

Applying encryption to buckets

Securing S3 objects at rest and in transit

Using other types of data stores

Relational Database Services (RDSes)

Managing failover in Amazon RDS

Taking automatic backup, RDS snapshots, and restore and read replicas

Writing to Amazon Aurora with multi-master capabilities

Storing columnar data on Amazon Redshift

Amazon DynamoDB for NoSQL database as a service

Chapter 6: AWS Services for Data Processing

Creating ETL jobs on AWS Glue

Features of AWS Glue

Getting hands-on with AWS Glue data catalog components.

Getting hands-on with AWS Glue ETL components

Querying S3 data using Athena

Processing real-time data using Kinesis data streams

Storing and transforming real-time data using Kinesis Data Firehose

Different ways of ingesting data from on-premises into AWS

AWS Storage Gateway

Snowball, Snowball Edge, and Snowmobile

AWS DataSync

Processing stored data on AWS

AWS EMR

AWS Batch

Section 3: Data Modeling

Chapter 7: Applying Machine Learning Algorithms

Introducing this chapter

Storing the training data

A word about ensemble models

Supervised learning

Working with regression models

Working with classification models

Forecasting models

Object2Vec

Unsupervised learning

Clustering

Anomaly detection

Dimensionality reduction

IP Insights

Textual analysis

Blazing Text algorithm

Sequence-to-sequence algorithm

Neural Topic Model (NTM) algorithm

Image processing

Image classification algorithm

Semantic segmentation algorithm

Object detection algorithm

Chapter 8: Evaluating and Optimizing Models

Introducing model evaluation

Evaluating classification models

Extracting metrics from a confusion matrix

Summarizing precision and recall

Evaluating regression models

Exploring other regression metrics

Model optimization

Grid search

Chapter 9: Amazon SageMaker Modeling

Creating notebooks in Amazon SageMaker

What is Amazon SageMaker?

Getting hands-on with Amazon SageMaker notebook instances

Getting hands-on with Amazon SageMaker's training and inference instances

Model tuning

Tracking your training jobs and selecting the best model

Choosing instance types in Amazon SageMaker.

Choosing the right instance type for a training job

Choosing the right instance type for an inference job

Securing SageMaker notebooks

Creating alternative pipelines with Lambda Functions

Creating and configuring a Lambda Function

Completing your configurations and deploying a Lambda Function

Working with Step Functions

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About Packt

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Index.

Notes:

Includes index.

Description based on print version record.

ISBN:

1-80056-843-6

OCLC:

1243537225