Python Machine Learning by Example, 3rd Edition

Book Description

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 serves as a comprehensive gateway into the world of machine learning (ML).

With six new chapters, including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks (CNNs), 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, the 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 machine learning Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed on 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.

中文：

书名：Python Machine Learning by Example, 3rd Edition

这是一本全面的指南，帮助您快速了解使用Python进行机器学习的最新进展，并提高您对机器学习(ML)算法和技术的理解

主要特点

• 潜心研究机器学习算法，解决当今数据科学家面临的复杂挑战
• 探索反映深度学习和强化学习发展的前沿内容
• 使用更新的Python库，如TensorFlow、PyTorch和SCRICKIT-学习端到端跟踪机器学习项目

图书描述

通过举例说明，Python机器学习是进入机器学习(ML)世界的综合门户。

这本书有六个新的章节，包括使用朴素贝叶斯开发电影推荐引擎，使用支持向量机识别人脸，使用人工神经网络预测股票价格，使用卷积神经网络(CNN)对服装图像进行分类，使用递归神经网络进行序列预测，以及利用强化学习进行决策，这本书已经根据最新的企业需求进行了相当大的更新。

同时，这本书提供了关于使用Python编程的ML的关键基础的可操作的见解。Hayden运用他的专业知识从头开始和使用库演示了用Python语言实现的算法。

每一章都介绍一个行业采用的应用程序。在实际示例的帮助下，您将了解ML技术在探索性数据分析、特征工程、分类、回归、集群和NLP等领域的机制。

到本书结束时，您将对ML生态系统有一个大致的了解，并将精通应用ML技术来解决问题的最佳实践。

你将学到什么

• 理解ML和数据科学中的重要概念
• Use Python to explore the world of data mining and analytics
• 使用ApacheSpark使用各种数据复杂性扩展模型培训
• 使用NLTK和Gensim等Python库深入研究文本分析和NLP
• Select and build an ML model and evaluate and optimize its performance
• 在Python、TensorFlow 2、PyTorch和SCRICKIT中从头开始实现ML算法-学习

这本书是为谁写的

如果你是一名机器学习爱好者、数据分析师或数据工程师，对机器学习非常感兴趣，并想开始从事机器学习任务，这本书适合你。

假设您已经具备了Python编码的先验知识，对统计概念有基本的了解将是有益的，尽管这并不是必需的。