Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You'll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you'll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. * Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building * Discover common neural network frameworks with Amazon SageMaker * Solve computer vision problems with Amazon Rekognition * Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.
Autorentext
ABOUT THE AUTHOR
ABHISHEK MISHRA has more than 19 years' experience across a broad range of enterprise technologies. He consults as a security and fraud solution architect with Lloyds Banking group PLC in London. He is the author of Amazon Web Services for Mobile Developers.
Klappentext
Harness the power of AWS Cloud machine learning services
Recent advances in storage, CPU, and GPU technology, coupled with the ease with which you can create virtual computing resources in the cloud, and the availability of Python libraries such as Pandas, Matplotlib, TensorFlow, and Scikit-learn, have made it possible to build and deploy machine learning (ML) systems at scale and get results in real-time. Machine Learning in the AWS Cloud offers an introduction to the machine learning capabilities of the Amazon Web Services ecosystem. The book is filled with illustrative examples that are designed to help with solutions to real-world regression and classification challenges. While prior experience with ML is not a requirement, some knowledge of Python and a basic knowledge of Amazon Web Services is a plus.
The authora noted expert on the topicincludes a review of fundamental machine learning concepts and explores the various types of ML systems. He explains how they are used, and the challenges you may face when grappling with ML solutions. The book highlights the machine learning services provided by Amazon Web Services as well as providing an overview of the basics of cloud computing and AWS offerings in the cloud-based machine learning space. The author walks you through the step-by-step process for using Amazon's machine learning services to implement image recognition, build chatbots, and train and deploy custom machine learning models to the AWS cloud.
- Improve your knowledge of the basics of machine learning and learn to use NumPy, Pandas, and Scikit-learn®
- Learn to visualize data with Matplotlib
- Learn to train and deploy machine learning models with Amazon SageMaker
- Learn to use Amazon Machine Learning
- Learn to use Amazon Lex®, Amazon Comprehend, and Amazon Rekognition
- Learn about the basics of AWS infrastructure and commonly used services such as Amazon S3, Amazon DynamoDB, Amazon Cognito, and AWS Lambda
ABOUT AMAZON WEB SERVICES
Amazon Web Services (AWS) is a secure cloud services platform that offers a broad set of global compute, storage, database, analytics, application, and deployment services to help businesses scale and grow. AWS Cloud products and solutions aid business organizations in building sophisticated applications with increased flexibility, scalability, and reliability.
Inhalt
Introduction xxiii
Part 1 Fundamentals of Machine Learning 1
Chapter 1 Introduction to Machine Learning 3
What is Machine Learning? 4
Tools Commonly Used by Data Scientists 4
Common Terminology 5
Real-World Applications of Machine Learning 7
Types of Machine Learning Systems 8
Supervised Learning 8
Unsupervised Learning 9
Semi-Supervised Learning 10
Reinforcement Learning 11
Batch Learning 11
Incremental Learning 12
Instance-based Learning 12
Model-based Learning 12
The Traditional Versus the Machine Learning Approach 13
A Rule-based Decision System 14
A Machine Learningbased System 17
Summary 25
Chapter 2 Data Collection and Preprocessing 27
Machine Learning Datasets 27
Scikit-learn Datasets 27
AWS Public Datasets 30
Kaggle.com Datasets 30
UCI Machine Learning Repository 30
Data Preprocessing Techniques 31
Obtaining an Overview of the Data 31
Handling Missing Values 42
Creating New Features 44
Transforming Numeric Features 46
One-Hot Encoding Categorical Features 47
Summary 50
Chapter 3 Data Visualization with Python 51
Introducing Matplotlib 51
Components of a Plot 54
Figure 55
Axes55
Axis 56
Axis Labels 56
Grids 57
Title 57
Common Plots 58
Histograms 58
Bar Chart 62
Grouped Bar Chart 63
Stacked Bar Chart 65
Stacked Percentage Bar Chart 67
Pie Charts 69
Box Plot 71
Scatter Plots 73
Summary 78
Chapter 4 Creating Machine Learning Models with Scikit-learn 79
Introducing Scikit-learn 79
Creating a Training and Test Dataset 80
K-Fold Cross Validation 84
Creating Machine Learning Models 86
Linear Regression 86
Support Vector Machines 92
Logistic Regression 101
Decision Trees 109
Summary 114
Chapter 5 Evaluating Machine Learning Models 115
Evaluating Regression Models 115
RMSE Metric 117
R2 Metric 119
Evaluating Classification Models 119
Binary Classification Models 119
Multi-Class Classification Models 126
Choosing Hyperparameter Values 131
Summary 132
Part 2 Machine Learning with Amazon Web Services 133
Chapter 6 Introduction to Amazon Web Services 135
What is Cloud Computing? 135
Cloud Service Models 136
Cloud Deployment Models 138
The AWS Ecosystem 139
Machine Learning Application Services 140
Machine Learning Platform Services 141
Support Services 142
Sign Up for an AWS Free-Tier Account 142
Step 1: Contact Information 143
Step 2: Payment Information 145
Step 3: Identity Verification 145
Step 4: Support Plan Selection 147
Step 5: Confirmation 148
Summary 148
Chapter 7 AWS Global Infrastructure 151
Regions and Availability Zones 151
Edge Locations 153
Accessing AWS 154
The AWS Management Console 156
Summary 160
Chapter 8 Identity and Access Management 161
Key Concepts 161
Root Account 161
User 162
Identity Federation 162
Group 163
Policy164
Role 164
Common Tasks 165