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