Hands-on machine learning with IBM Watson : leverage IBM Watson to implement machine learning techniques and algorithms using Python /: leverage IBM Watson to implement machine learning techniques and algorithms using Python. (2019)

• Record Type: Book
• Further Information: Note: James D. Miller.
• Authors: (Software consultant), Miller, James D
• Contents: 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
• Publisher Details: Birmingham, UK : Packt Publishing
• Publication Date: 2019
• Extent: 1 online resource, illustrations
• Subjects: 006.31; Machine learning; Python (Computer program language); Watson (Computer); Computer algorithms; Electronic books
• Languages: English
• ISBNs: 9781789616279; 1789616271
• Related ISBNs: 9781789611854
• Notes: Note: Includes bibliographical references.; Note: Description based on online resource; title from title page (Safari, viewed May 8, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.