Python Machine Learning by Example : Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3/e (Paperback) (Python 機器學習實例：使用 Python、TensorFlow 2、PyTorch 和 scikit-learn 建立智能系統，第三版)

Liu, Yuxi (Hayden)

Python Machine Learning by Example : Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3/e (Paperback)

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商品描述

A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques

Key Features

  • Dive into machine learning algorithms to solve the complex challenges faced by data scientists today
  • Explore cutting edge content reflecting deep learning and reinforcement learning developments
  • Use updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-end

Book Description

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).

With six new chapters, on topics including movie recommendation engine development with Naive Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.

At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.

Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.

By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.

What you will learn

  • Understand the important concepts in ML and data science
  • Use Python to explore the world of data mining and analytics
  • Scale up model training using varied data complexities with Apache Spark
  • Delve deep into text analysis and NLP using Python libraries such NLTK and Gensim
  • Select and build an ML model and evaluate and optimize its performance
  • Implement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learn

Who this book is for

If you're a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you.

Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.

商品描述(中文翻譯)

《Python機器學習實例》第三版是一本全面的指南，讓您了解最新的Python實用機器學習發展，並提升對機器學習算法和技術的理解。

主要特點：

- 深入研究機器學習算法，解決數據科學家今天面臨的複雜挑戰
- 探索反映深度學習和強化學習發展的尖端內容
- 使用更新的Python庫，如TensorFlow、PyTorch和scikit-learn，從頭到尾跟踪機器學習項目

書籍描述：

《Python機器學習實例》第三版是進入機器學習世界的全面入門書籍。

新增了六個章節，包括使用朴素貝葉斯開發電影推薦引擎、使用支持向量機識別人臉、使用人工神經網絡預測股票價格、使用卷積神經網絡分類服裝圖像、使用循環神經網絡預測序列、以及利用強化學習做出決策。這本書已經根據最新的企業需求進行了相當大的更新。

同時，本書提供了有關Python編程中機器學習基礎的可行洞察。Hayden運用自己的專業知識，展示了使用Python從頭開始和使用庫實現算法的示例。

每一章都通過一個行業採用的應用案例進行演示。通過實際例子的幫助，您將了解到在探索性數據分析、特徵工程、分類、回歸、聚類和自然語言處理等領域中機器學習技術的運作原理。

通過閱讀本書，您將對機器學習生態系統有一個全面的了解，並熟悉應用機器學習技術解決問題的最佳實踐。

學到什麼：

- 理解機器學習和數據科學的重要概念
- 使用Python探索數據挖掘和分析的世界
- 使用Apache Spark應對不同數據複雜性的模型訓練
- 深入研究文本分析和自然語言處理，使用Python庫如NLTK和Gensim
- 選擇並構建機器學習模型，評估和優化其性能
- 在Python、TensorFlow 2、PyTorch和scikit-learn中從頭實現機器學習算法

適合對象：

如果您是機器學習愛好者、數據分析師或數據工程師，對機器學習非常熱衷，並且想開始進行機器學習任務，那麼這本書適合您。

假設您具備Python編程的基礎知識，對統計概念有基本的了解將有所幫助，但這不是必需的。

作者簡介

Yuxi (Hayden) Liu is a machine learning software engineer at Google. Previously he worked as a machine learning scientist in a variety of data-driven domains and applied his machine learning expertise in computational advertising, marketing, and cybersecurity.

Hayden is the author of a series of machine learning books and an education enthusiast. His first book, the first edition of Python Machine Learning By Example, was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. His other books include R Deep Learning Projects, Hands-On Deep Learning Architectures with Python, and PyTorch 1.x Reinforcement Learning Cookbook.

作者簡介(中文翻譯)

Yuxi (Hayden) Liu 是 Google 的機器學習軟體工程師。之前，他在多個以數據驅動的領域擔任機器學習科學家，並將他的機器學習專業知識應用於計算廣告、行銷和網絡安全領域。

Hayden 是一系列機器學習書籍的作者，也是一位教育熱衷者。他的第一本書《Python Machine Learning By Example》的第一版在2017年和2018年在亞馬遜上的該類別中排名第一暢銷書籍，並被翻譯成多種語言。他的其他書籍包括《R Deep Learning Projects》、《Hands-On Deep Learning Architectures with Python》和《PyTorch 1.x Reinforcement Learning Cookbook》。

目錄大綱

  1. Getting Started with Machine Learning and Python
  2. Building a Movie Recommendation Engine with Naive Bayes
  3. Recognizing Faces with Support Vector Machine
  4. Predicting Online Ad Click-Through with Tree-Based Algorithms
  5. Predicting Online Ad Click-Through with Logistic Regression
  6. Scaling Up Prediction to Terabyte Click Logs
  7. Predicting Stock Prices with Regression Algorithms
  8. Predicting Stock Prices with Artificial Neural Networks
  9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques
  10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
  11. Machine Learning Best Practices
  12. Categorizing Images of Clothing with Convolutional Neural Networks
  13. Making Predictions with Sequences Using Recurrent Neural Networks
  14. Making Decisions in Complex Environments with Reinforcement Learning

目錄大綱(中文翻譯)

- 使用Python入門機器學習
- 使用Naive Bayes建立電影推薦引擎
- 使用支持向量機識別人臉
- 使用基於樹的演算法預測線上廣告點擊率
- 使用邏輯回歸預測線上廣告點擊率
- 將預測擴展到TB級別的點擊日誌
- 使用回歸演算法預測股票價格
- 使用人工神經網絡預測股票價格
- 使用文本分析技術挖掘20個新聞組數據集
- 使用聚類和主題建模發現新聞組數據集中的潛在主題
- 機器學習最佳實踐
- 使用卷積神經網絡對服裝圖像進行分類
- 使用循環神經網絡對序列進行預測
- 使用強化學習在複雜環境中做出決策

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