Hands-On Machine Learning with IBM Watson
暫譯: 實作機器學習與 IBM Watson
Miller, James D.
- 出版商: Packt Publishing
- 出版日期: 2019-03-29
- 售價: $1,830
- 貴賓價: 9.5 折 $1,739
- 語言: 英文
- 頁數: 288
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1789611857
- ISBN-13: 9781789611854
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相關分類:
Machine Learning
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相關主題
商品描述
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.
商品描述(中文翻譯)
IBM Cloud 是一系列用於數據分析的雲端計算服務,利用機器學習和人工智慧 (AI)。本書是一本完整的指南,幫助您熟悉在 IBM Cloud 上使用 Python 進行機器學習。
《Hands-On Machine Learning with IBM Watson》從監督式和非監督式機器學習概念開始,並提供 IBM Cloud 和 Watson Machine Learning 的概述。您將深入了解在 IBM Cloud 中運行各種技術,例如 K-means 聚類、K 最近鄰 (KNN) 和時間序列預測,並通過實際案例進行學習。接下來,本書將幫助您深入了解如何在 Watson Studio 中創建 Spark 管道。您還將學習在 IBM Cloud 上使用 TensorFlow 的深度學習和神經網絡原則。在自然語言處理 (NLP) 技術的幫助下,您將能夠提升構建聊天機器人的技能。在後面的章節中,您將涵蓋三個強大的案例研究,包括面部表情分類平台、自動化岩相分類和多生物識別身份驗證平台,幫助您熟悉這些方法論。
在本書結束時,您將準備好在 IBM Cloud 上構建高效的機器學習解決方案,並利用實際案例從手頭數據中提取見解。