Hands-On Machine Learning with IBM Watson: Leverage IBM Watson to implement machine learning techniques and algorithms using Python
bLearn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services/b h4Key Features/h4 ulliImplement data science and machine learning techniques to draw insights from real-world data /li liUnderstand what IBM Cloud platform can help you to implement cognitive...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham
Packt Publishing Limited
2019
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| Edition: | 1 |
| Subjects: | |
| Summary: | bLearn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services/b h4Key Features/h4 ulliImplement data science and machine learning techniques to draw insights from real-world data /li liUnderstand what IBM Cloud platform can help you to implement cognitive insights within applications /li liUnderstand the role of data representation and feature extraction in any machine learning system/li/ul h4Book Description/h4 IBM 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. h4What you will learn/h4 ulliUnderstand key characteristics of IBM machine learning services /li liRun supervised and unsupervised techniques in the cloud /li liUnderstand how to create a Spark pipeline in Watson Studio /li liImplement deep learning and neural networks on the IBM Cloud with TensorFlow /li liCreate a complete, cloud-based facial expression classification solution /li liUse biometric traits to build a cloud-based human identification system/li/ul h4Who this book is for/h4 This 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 |
| Physical Description: | 1 Online-Ressource (288 Seiten) |
| ISBN: | 9781789616279 |
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| 520 | |a bLearn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services/b h4Key Features/h4 ulliImplement data science and machine learning techniques to draw insights from real-world data /li liUnderstand what IBM Cloud platform can help you to implement cognitive insights within applications /li liUnderstand the role of data representation and feature extraction in any machine learning system/li/ul h4Book Description/h4 IBM 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. | ||
| 520 | |a 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. | ||
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| spelling | D. Miller, James Verfasser aut Hands-On Machine Learning with IBM Watson Leverage IBM Watson to implement machine learning techniques and algorithms using Python D. Miller, James 1 Birmingham Packt Publishing Limited 2019 1 Online-Ressource (288 Seiten) txt rdacontent c rdamedia cr rdacarrier bLearn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services/b h4Key Features/h4 ulliImplement data science and machine learning techniques to draw insights from real-world data /li liUnderstand what IBM Cloud platform can help you to implement cognitive insights within applications /li liUnderstand the role of data representation and feature extraction in any machine learning system/li/ul h4Book Description/h4 IBM 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. h4What you will learn/h4 ulliUnderstand key characteristics of IBM machine learning services /li liRun supervised and unsupervised techniques in the cloud /li liUnderstand how to create a Spark pipeline in Watson Studio /li liImplement deep learning and neural networks on the IBM Cloud with TensorFlow /li liCreate a complete, cloud-based facial expression classification solution /li liUse biometric traits to build a cloud-based human identification system/li/ul h4Who this book is for/h4 This 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 COMPUTERS / Computer Vision & Pattern Recognition COMPUTERS / Enterprise Applications / Collaboration Software |
| spellingShingle | D. Miller, James Hands-On Machine Learning with IBM Watson Leverage IBM Watson to implement machine learning techniques and algorithms using Python COMPUTERS / Computer Vision & Pattern Recognition COMPUTERS / Enterprise Applications / Collaboration Software |
| title | Hands-On Machine Learning with IBM Watson Leverage IBM Watson to implement machine learning techniques and algorithms using Python |
| title_auth | Hands-On Machine Learning with IBM Watson Leverage IBM Watson to implement machine learning techniques and algorithms using Python |
| title_exact_search | Hands-On Machine Learning with IBM Watson Leverage IBM Watson to implement machine learning techniques and algorithms using Python |
| title_full | Hands-On Machine Learning with IBM Watson Leverage IBM Watson to implement machine learning techniques and algorithms using Python D. Miller, James |
| title_fullStr | Hands-On Machine Learning with IBM Watson Leverage IBM Watson to implement machine learning techniques and algorithms using Python D. Miller, James |
| title_full_unstemmed | Hands-On Machine Learning with IBM Watson Leverage IBM Watson to implement machine learning techniques and algorithms using Python D. Miller, James |
| title_short | Hands-On Machine Learning with IBM Watson |
| title_sort | hands on machine learning with ibm watson leverage ibm watson to implement machine learning techniques and algorithms using python |
| title_sub | Leverage IBM Watson to implement machine learning techniques and algorithms using Python |
| topic | COMPUTERS / Computer Vision & Pattern Recognition COMPUTERS / Enterprise Applications / Collaboration Software |
| topic_facet | COMPUTERS / Computer Vision & Pattern Recognition COMPUTERS / Enterprise Applications / Collaboration Software |
| work_keys_str_mv | AT dmillerjames handsonmachinelearningwithibmwatsonleverageibmwatsontoimplementmachinelearningtechniquesandalgorithmsusingpython |