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

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)

全面指南，幫助您掌握最新的實用機器學習發展，提升對機器學習（ML）演算法和技術的理解

主要特點


  • 深入探討機器學習演算法，以解決當今數據科學家面臨的複雜挑戰

  • 探索反映深度學習和強化學習發展的前沿內容

  • 使用更新的 Python 函式庫，如 TensorFlow、PyTorch 和 scikit-learn，追蹤機器學習專案的全過程

書籍描述

《Python 機器學習實例，第三版》是進入機器學習（ML）世界的全面門戶。

本書新增六個章節，涵蓋主題包括使用 Naive Bayes 開發電影推薦引擎、使用支持向量機識別面孔、使用人工神經網絡預測股價、使用卷積神經網絡對服裝圖像進行分類、使用遞歸神經網絡進行序列預測，以及利用強化學習進行決策，書籍已根據最新的企業需求進行了顯著更新。

同時，本書提供了有關使用 Python 程式設計的 ML 重要基礎知識的可行見解。Hayden 應用他的專業知識，展示了在 Python 中從零開始和使用函式庫實現演算法的過程。

每一章都介紹了一個行業採用的應用。透過現實的例子，您將了解在探索性數據分析、特徵工程、分類、回歸、聚類和自然語言處理（NLP）等領域中，ML 技術的運作機制。

在這本 ML Python 書籍的結尾，您將對 ML 生態系統有一個全面的了解，並熟悉應用 ML 技術解決問題的最佳實踐。

您將學到什麼


  • 理解 ML 和數據科學中的重要概念

  • 使用 Python 探索數據挖掘和分析的世界

  • 使用 Apache Spark 擴展模型訓練，處理各種數據複雜性

  • 深入使用 Python 函式庫如 NLTK 和 Gensim 進行文本分析和 NLP

  • 選擇並構建 ML 模型，評估和優化其性能

  • 在 Python、TensorFlow 2、PyTorch 和 scikit-learn 中從零開始實現 ML 演算法

本書適合誰

如果您是機器學習愛好者、數據分析師或數據工程師，對機器學習充滿熱情並希望開始進行機器學習任務，這本書適合您。

假設您具備 Python 編碼的基本知識，對統計概念的基本熟悉將是有益的，雖然這並不是必要的。

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)

**劉宇熙（Hayden Liu）**是Google的一名機器學習軟體工程師。之前，他在多個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、行銷和網路安全。

Hayden是多本機器學習書籍的作者，也是教育熱衷者。他的第一本書《Python Machine Learning By Example》的第一版在2017年和2018年被評為亞馬遜該類別的暢銷書第一名，並被翻譯成多種語言。他的其他著作包括《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)


  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

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