Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning servicesKey FeaturesImplement data science and machine learning techniques to draw insights from real-world dataUnderstand what IBM Cloud platform can help you to implement cognitive insights within applicationsUnderstand the role of data representation and feature extraction in any machine learning systemBook DescriptionIBM 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 learnUnderstand key characteristics of IBM machine learning servicesRun supervised and unsupervised techniques in the cloudUnderstand how to create a Spark pipeline in Watson StudioImplement deep learning and neural networks on the IBM Cloud with TensorFlowCreate a complete, cloud-based facial expression classification solutionUse biometric traits to build a cloud-based human identification systemWho this book is forThis 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.
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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.
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Table of ContentsIntroduction to IBM CloudFeature Extraction – A Bag of TricksSupervised Machine Learning Models for Your DataImplementing Unsupervised AlgorithmsMachine Learning Workouts on IBM CloudUsing SPARK with IBM Watson StudioDeep Learning Using TensorFlow on the IBM CloudCreating a Facial Expression Platform on the IBM CloudAutomated Classification of Lithofacies Formation Using Machine LearningBuilding a Cloud-Based Multi-Biometric Identity Authentication Platform
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Produktdetaljer

ISBN
9781789611854
Publisert
2019-03-29
Utgiver
Vendor
Packt Publishing Limited
Høyde
93 mm
Bredde
75 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet

Forfatter

Om bidragsyterne

James D. Miller is an innovator and accomplished senior project lead and solution architect with 37 years' experience of extensive design and development across multiple platforms and technologies. Roles include leveraging his consulting experience to provide hands-on leadership in all phases of advanced analytics and related technology projects, providing recommendations for process improvement, report accuracy, the adoption of disruptive technologies, enablement, and insight identification. He has also written a number of books, including Statistics for Data Science; Mastering Predictive Analytics with R, Second Edition; Big Data Visualization; Learning Watson Analytics; and many more.