Real-World Machine Learning

Henrik Brink, Joseph Richards, Mark Fetherolf

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商品描述

Summary

Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Machine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand.

About the Book

Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems.

What's Inside

Predicting future behavior
Performance evaluation and optimization
Analyzing sentiment and making recommendations
About the Reader

No prior machine learning experience assumed. Readers should know Python.

About the Authors

Henrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning.

Table of Contents

THE MACHINE-LEARNING WORKFLOW
What is machine learning?
Real-world data
Modeling and prediction
Model evaluation and optimization
Basic feature engineering
PRACTICAL APPLICATION
Example: NYC taxi data
Advanced feature engineering
Advanced NLP example: movie review sentiment
Scaling machine-learning workflows
Example: digital display advertising

商品描述(中文翻譯)

《實戰機器學習》是一本實用指南,旨在教導開發人員如何執行機器學習專案。本書不會過度講解學術理論和複雜數學,而是介紹機器學習的日常實踐,讓你能成功建立和部署強大的機器學習系統。

購買印刷版書籍還包括一本免費的電子書(PDF、Kindle和ePub格式),由Manning Publications提供。

關於技術:
機器學習系統可以幫助你在數據中找到有價值的洞見和模式,這是傳統方法無法辨識的。在現實世界中,機器學習技術可以幫助你識別趨勢、預測行為並提出基於事實的建議。這是一個熱門且不斷發展的領域,對機器學習開發人員的需求很高。

關於本書:
《實戰機器學習》將教授你成為一名成功的機器學習從業者所需的概念和技巧,而不會過度講解抽象理論和複雜數學。通過使用Python進行實際的例子,你將建立在數據獲取和建模、分類和回歸方面的技能。你還將探索最重要的任務,如模型驗證、優化、可擴展性和實時流式處理。完成後,你將準備好成功地建立、部署和維護自己的強大機器學習系統。

內容包括:
- 預測未來行為
- 性能評估和優化
- 分析情感並提出建議

讀者需求:
不需要先前的機器學習經驗,讀者應該熟悉Python。

關於作者:
Henrik Brink、Joseph Richards和Mark Fetherolf是經驗豐富的數據科學家,從事日常的機器學習實踐。

目錄:
機器學習工作流程
- 什麼是機器學習?
- 現實世界的數據
- 建模和預測
- 模型評估和優化
- 基本特徵工程

實際應用
- 例子:紐約出租車數據
- 高級特徵工程
- 高級自然語言處理例子:電影評論情感
- 擴展機器學習工作流程
- 例子:數字顯示廣告