Title Page Copyright and Credits Hands-On Machine Learning with IBM Watson  About Packt Why subscribe? 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 Another Book You May Enjoy Leave a review - let other readers know what you think