Hands-On Machine Learning with IBM Watson

Book Description

Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services

Key Features

  • Implement data science and machine learning techniques to draw insights from real-world data
  • Understand what IBM Cloud platform can help you to implement cognitive insights within applications
  • Understand the role of data representation and feature extraction in any machine learning system

Book Description

IBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python.

Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies.

By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples.

What you will learn

  • Understand key characteristics of IBM machine learning services
  • Run supervised and unsupervised techniques in the cloud
  • Understand how to create a Spark pipeline in Watson Studio
  • Implement deep learning and neural networks on the IBM Cloud with TensorFlow
  • Create a complete, cloud-based facial expression classification solution
  • Use biometric traits to build a cloud-based human identification system

Who this book is for

This beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. Basic knowledge of Python and some understanding of machine learning will be useful.

Publisher Resources

Download Example Code

Table of Contents

  1. Title Page
  2. Copyright and Credits
    1. Hands-On Machine Learning with IBM Watson 
  3. About Packt
    1. Why subscribe?
    2. Packt.com
  4. Contributors
    1. About the author
    2. About the reviewer
    3. Packt is searching for authors like you
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
      1. Download the example code files
      2. Download the color images
      3. Conventions used
    4. Get in touch
      1. Reviews
  6. Section 1: Introduction and Foundation
  7. Introduction to IBM Cloud
    1. Understanding IBM Cloud
      1. Prerequisites 
    2. Accessing the IBM Cloud 
    3. Cloud resources 
    4. The IBM Cloud and Watson Machine Learning services
    5. Setting up the environment
    6. Watson Studio Cloud 
      1. Watson Studio architecture and layout 
      2. Establishing context 
    7. Setting up a new project 
    8. Data visualization tutorial 
    9. Summary 
  8. Feature Extraction - A Bag of Tricks
    1. Preprocessing
      1. The data refinery
      2. Data
      3. Adding the refinery
      4. Refining data by using commands
    2. Dimensional reduction
    3. Data fusion
      1. Catalog setup
      2. Recommended assets
    4. A bag of tricks
    5. Summary
  9. Supervised Machine Learning Models for Your Data
    1. Model selection
      1. IBM Watson Studio Model Builder
      2. Using the model builder
      3. Training data
      4. Guessing which technique to use
      5. Deployment
      6. Model builder deployment steps
    2. Testing the model
      1. Continuous learning and model evaluation
    3. Classification
      1. Binary classification
      2. Multiclass classification
    4. Regression
    5. Testing the predictive capability
    6. Summary
  10. Implementing Unsupervised Algorithms
    1. Unsupervised learning
      1. Watson Studio, machine learning flows, and KMeans
      2. Getting started
        1. Creating an SPSS modeler flow
        2. Additional node work
        3. Training and testing
        4. SPSS flow and K-means
        5. Exporting model results
    2. Semi-supervised learning
    3. Anomaly detection
      1. Machine learning based approaches
    4. Online or batch learning
    5. Summary
  11. Section 2: Tools and Ingredients for Machine Learning in IBM Cloud
  12. Machine Learning Workouts on IBM Cloud
    1. Watson Studio and Python
    2. Setting up the environment
      1. Try it out
    3. Data cleansing and preparation
    4. K-means clustering using Python
      1. The Python code
      2. Observing the results
      3. Implementing in Watson
      4. Saving your work
    5. K-nearest neighbors
      1. The Python code
      2. Implementing in Watson
      3. Exploring Markdown text
    6. Time series prediction example
      1. Time series analysis
        1. Setup
        2. Data preprocessing
        3. Indexing for visualization
        4. Visualizations
        5. Forecasting sales
        6. Validation
    7. Summary
  13. Using Spark with IBM Watson Studio
    1. Introduction to Apache Spark
    2. Watson Studio and Spark
    3. Creating a Spark-enabled notebook
    4. Creating a Spark pipeline in Watson Studio
      1. What is a pipeline?
      2. Pipeline objectives
      3. Breaking down a pipeline example
    5. Data preparation
      1. The pipeline
    6. A data analysis and visualization example
      1. Setup
        1. Getting the data
        2. Loading the data
      2. Exploration
      3. Extraction
      4. Plotting
      5. Saving
      6. Downloading your notebook
    7. Summary
  14. Deep Learning Using TensorFlow on the IBM Cloud
    1. Introduction to deep learning 
    2. TensorFlow basics 
    3. Neural networks and TensorFlow 
    4. An example 
      1. Creating the new project
      2. Notebook asset type
      3. Running the imported notebook
      4. Reviewing the notebook
    5. TensorFlow and image classifications
      1. Adding the service
      2. Required modules
      3. Using the API key in code
    6. Additional preparation
      1. Upgrading Watson
      2. Images
      3. Code examination
      4. Accessing the model
      5. Detection
      6. Classification and output
      7. Objects detected
      8. Now the fun part
      9. Save and share your work
    7. Summary
  15. Section 3: Real-Life Complete Case Studies
  16. Creating a Facial Expression Platform on IBM Cloud
    1. Understanding facial expression classification
      1. Face detection
      2. Facial expression analysis
      3. TBM
    2. Exploring expression databases
      1. Training with the Watson Visual Recognition service 
    3. Preprocessing faces
      1. Preparing the training data
      2. Negative or non-positive classing 
    4. Preparing the environment
      1. Project assets
      2. Creating classes for our model
      3. Automatic labeling
    5. Learning the expression classifier
      1. Evaluating the expression classifier
      2. Viewing the model training results
      3. Testing the model
      4. Test scores
        1. Test the model
        2. Improving the model
      5. More training data
      6. Adding more classes
      7. Results
    6. Summary
  17. The Automated Classification of Lithofacies Formation Using ML
    1. Understanding lithofacies
      1. Depositional environments
      2. Lithofacies formation
        1. Our use case
    2. Exploring the data
      1. Well logging
      2. Log ASCII Standard (LAS)
      3. Loading the data asset
      4. Data asset annotations
      5. Profiling the data
      6. Using a notebook and Python instead
      7. Loading the data
      8. Visualizations
      9. Box plotting
      10. Histogram
      11. The scatter matrix
    3. Training the classifier
      1. Building a logistic regression model
      2. Building a KNN model
      3. Building a Gaussian Naive Bayes model
      4. Building a support vector machine model
      5. Building a decision tree model
      6. Summing them up
    4. Evaluating the classifier
      1. A disclaimer of sorts
      2. Understanding decision trees
    5. Summary
  18. Building a Cloud-Based Multibiometric Identity Authentication Platform
    1. Understanding biometrics
      1. Making a case
      2. Popular use cases
      3. Privacy concerns
      4. Components of a biometric authentication solution
    2. Exploring biometric data
      1. Specific Individual identification
        1. The Challenge of Biometric Data Use
      2. Sample sizing
    3. Feature extraction
      1. Biometric recognition
    4. Multimodal fusion
    5. Our example
      1. Premise
      2. Data preparation
      3. Project setup
      4. Creating classes
      5. Training the model
      6. Testing our project
      7. Guidelines for good training
      8. Implementation
    6. Summary
  19. Another Book You May Enjoy
    1. Leave a review - let other readers know what you think

Product Information

  • Title: Hands-On Machine Learning with IBM Watson
  • Author(s): James D. Miller
  • Release date: March 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781789611854