Hands-on machine learning with IBM Watson : leverage IBM Watson to implement machine learning techniques and algorithms using Python / James D. Miller

Includes bibliographical references

Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Introduction and Foundation; Chapter 1: 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 ; Chapter 2: Feature Extraction -- A Bag of Tricks; Preprocessing; The data refinery; Data

Adding the refineryRefining data by using commands; Dimensional reduction; Data fusion; Catalog setup; Recommended assets; A bag of tricks; Summary; Chapter 3: 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

Chapter 4: Implementing Unsupervised AlgorithmsUnsupervised 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; Chapter 5: 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 PythonThe 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; Chapter 6: 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 exampleData preparation; The pipeline; A data analysis and visualization example; Setup; Getting the data; Loading the data; Exploration; Extraction; Plotting; Saving; Downloading your notebook; Summary; Chapter 7: 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

A practical guide on Machine learning with IBM cloud to act as a solid yet concise reference for the readers. You will learn about the role of data representation and feature extraction in machine learning. This book will help you learn how to use the IBM Cloud and Watson Machine learning service to develop real-world machine learning solutions