This book will help you master your skills in various artificial intelligence and machine learning services available on AWS. Through practical hands-on examples, you'll learn how to use these services to generate impressive results. You will have a tremendous understanding of how to use a wide range of AWS services in your own organization.

• Cover
• Title Page
• Copyright and Credits
• Dedication
• About Packt
• Contributors
• Table of Contents
• Preface
• Section 1: Machine Learning on AWS
• Chapter 1: Getting Started with Machine Learning for AWS
  • How AWS empowers data scientists
    • Using AWS tools for machine learning
  • Identifying candidate problems that can be solved using machine learning
  • Machine learning project life cycle
    • Data gathering
    • Evaluation metrics
    • Algorithm selection
  • Deploying models
  • Summary
  • Exercise
• Section 2: Implementing Machine Learning Algorithms at Scale on AWS
• Chapter 2: Classifying Twitter Feeds with Naive Bayes
  • Classification algorithms
    • Feature types
      • Nominal features
      • Ordinal features
      • Continuous features
  • Naive Bayes classifier
    • Bayes' theorem
      • Posterior  
      • Likelihood 
      • Prior probability 
      • Evidence 
      • How the Naive Bayes algorithm works
  • Classifying text with language models
    • Collecting the tweets
    • Preparing the data
    • Building a Naive Bayes model through SageMaker notebooks
    • Naïve Bayes model on SageMaker notebooks using Apache Spark
    • Using SageMaker's BlazingText built-in ML service
  • Naive Bayes – pros and cons
  • Summary
  • Exercises
• Chapter 3: Predicting House Value with Regression Algorithms
  • Predicting the price of houses
  • Understanding linear regression
    • Linear least squares estimation
    • Maximum likelihood estimation
    • Gradient descent 
  • Evaluating regression models 
    • Mean absolute error
    • Mean squared error
    • Root mean squared error
    • R-squared
  • Implementing linear regression through scikit-learn
  • Implementing linear regression through Apache Spark
  • Implementing linear regression through SageMaker's linear Learner
  • Understanding logistic regression
    • Logistic regression in Spark
  • Pros and cons of linear models
  • Summary
• Chapter 4: Predicting User Behavior with Tree-Based Methods
  • Understanding decision trees
    • Recursive splitting
      • Types of decision trees
    • Cost functions 
      • Gini Impurity
      • Information gain
    • Criteria to stop splitting trees
  • Understanding random forest algorithms
  • Understanding gradient boosting algorithms
  • Predicting clicks on log streams
    • Introduction to Elastic MapReduce (EMR)
    • Training with Apache Spark on EMR
      • Getting the data
      • Preparing the data
        • Categorical encoding 
        • One-hot encoding
      • Training a model
      • Evaluating our model
        • Area Under ROC Curve
        • Area under the precision-recall curve
      • Training tree ensembles on EMR
    • Training gradient-boosted trees with the SageMaker services
      • Preparing the data
        • Training with SageMaker XGBoost   
        • Applying and evaluating the model
  • Summary
  • Exercises
• Chapter 5: Customer Segmentation Using Clustering Algorithms
  • Understanding How Clustering Algorithms Work
    • k-means clustering 
      • Euclidean distance
      • Manhattan distance
    • Hierarchical clustering
      • Agglomerative clustering
      • Divisive clustering
  • Clustering with Apache Spark on EMR
    • Clustering with Spark and SageMaker on EMR
    • Understanding the purpose of the IAM role
  • Summary
  • Exercises
• Chapter 6: Analyzing Visitor Patterns to Make Recommendations
  • Making theme park attraction recommendations through Flickr data
  • Collaborative filtering
    • Memory-based approach
    • Model-based approach
      • Matrix factorization
      • Stochastic gradient descent
      • Alternating Least Squares 
  • Finding recommendations through Apache Spark's ALS
    • Data gathering and exploration
    • Training the model
    • Getting recommendations
  • Recommending attractions through SageMaker Factorization Machines
    • Preparing the dataset for learning
    • Training the model
    • Getting recommendations
  • Summary
  • Exercises
• Section 3: Deep Learning
• Chapter 7: Implementing Deep Learning Algorithms
  • Understanding deep learning
  • Applications of deep learning
    • Self-driving cars
    • Learning to play video games using a deep learning algorithm
  • Understanding deep learning algorithms
    • Neural network algorithms
      • Activation function
      • Backpropagation
    • Introduction to deep neural networks
  • Understanding convolutional neural networks
  • Summary
  • Exercises
• Chapter 8: Implementing Deep Learning with TensorFlow on AWS
  • About TensorFlow
  • TensorFlow as a general machine learning library
  • Training and serving the TensorFlow model through SageMaker
  • Creating a custom neural net with TensorFlow 
  • Summary
  • Exercises
• Chapter 9: Image Classification and Detection with SageMaker
  • Introducing Amazon SageMaker for image classification
  • Training a deep learning model using Amazon SageMaker
  • Classifying images using Amazon SageMaker
  • Summary
  • Exercises
• Section 4: Integrating Ready-Made AWS Machine Learning Services
• Chapter 10: Working with AWS Comprehend
  • Introducing Amazon Comprehend
  • Accessing AmazonComprehend
  • Named-entity recognition using Comprehend
  • Sentiment analysis using Comprehend
  • Text classification using Comprehend
  • Summary
  • Exercise
• Chapter 11: Using AWS Rekognition
  • Introducing Amazon Rekognition
  • Implementing object and scene detection
  • Implementing facial analysis
    • Other Rekognition services
      • Image moderation
      • Celebrity recognition
      • Face comparison
  • Summary
  • Exercise
• Chapter 12: Building Conversational Interfaces Using AWS Lex
  • Introducing Amazon Lex
  • Building a custom chatbot using Amazon Lex
  • Summary
  • Exercises
• Section 5: Optimizing and Deploying Models through AWS
• Chapter 13: Creating Clusters on AWS
  • Choosing your instance types
    • On-demand versus spot instance pricing
    • Reserved pricing
    • Amazon Machine Images (AMIs)
    • Deep learning hardware
  • Distributed deep learning
    • Model versus data parallelization
    • Distributed TensorFlow
    • Distributed learning through Apache Spark
      • Data parallelization
      • Model parallelization
      • Distributed hyperparameter tuning
      • Distributed predictions at scale
    • Parallelization in SageMaker
  • Summary
• Chapter 14: Optimizing Models in Spark and SageMaker
  • The importance of model optimization
  • Automatic hyperparameter tuning
  • Hyperparameter tuning in Apache Spark
  • Hyperparameter tuning in SageMaker
  • Summary
  • Exercises
• Chapter 15: Tuning Clusters for Machine Learning
  • Introduction to the EMR architecture
    • Apache Hadoop
    • Apache Spark
    • Apache Hive
    • Presto
    • Apache HBase
    • Yet Another Resource Negotiator
  • Tuning EMR for different applications
    • Configuring application properties
      • Maximize Resource Allocation
      • The AWS Glue Catalog
  • Managing data pipelines with Glue
    • Creating tables with Glue
    • Accessing Glue tables in Spark
  • Summary
• Chapter 16: Deploying Models Built in AWS
  • SageMaker model deployment
  • Apache Spark model deployment
  • Summary
  • Exercises
• Appendix: Getting Started with AWS
• Other Books You May Enjoy
• Index