掌握Python机器学习：《Python Machine Learning By Example》第三版

Python
1星 需积分: 21 6 下载量 89 浏览量 更新于2024-12-25 收藏 64.08MB ZIP 举报
资源摘要信息:"Packt出版社发行的《Python Machine Learning By Example Third Edition》是一本专注于Python编程语言和机器学习示例的书籍。本书由Hayden Liu编写，旨在为机器学习爱好者提供一个全面的实践课程，帮助他们更好地理解机器学习的基础概念、技术以及算法。 Python是一种流行的编程语言，由于其简洁的语法和强大的库支持，在数据科学和机器学习领域得到了广泛应用。机器学习作为人工智能的一个分支，它使计算机系统能够通过经验学习和改进，而不需要明确的编程来适应新任务。 《Python Machine Learning By Example Third Edition》的第三版包含了大量的更新，反映了机器学习领域最新的研究成果和应用实践。本书通过实例学习的方法，将理论知识和实践相结合，使得读者不仅能够理解机器学习的原理，还能掌握如何在实际问题中应用机器学习技术。 书中可能会涵盖以下知识点： 1. 机器学习基础：介绍机器学习的基本概念，包括监督学习、非监督学习、半监督学习和强化学习等主要类型。 2. 数据预处理：讨论如何准备和处理数据，这一步骤对于任何机器学习项目都至关重要。可能涉及数据清洗、特征选择、数据规范化和数据转换等技术。 3. 模型构建：详细讲解构建机器学习模型的过程，包括模型选择、训练和验证。可能会介绍一些常用的机器学习算法，比如线性回归、决策树、随机森林和神经网络等。 4. 模型评估与优化：阐述如何评估模型的性能，以及如何调整模型参数以提高准确度。这部分内容可能包含交叉验证、超参数调整和模型选择标准等内容。 5. 实际应用案例：通过实际案例来展示如何应用机器学习技术解决现实世界的问题。这可能包括文本分析、图像识别、推荐系统等领域的应用。 6. 最新技术趋势：鉴于这是第三版，书中可能会讨论机器学习领域的最新趋势和技术，如深度学习、迁移学习和强化学习等。 7. 工具和库：介绍在Python中进行机器学习所用到的主要工具和库，例如scikit-learn、TensorFlow、Keras和PyTorch等。这些工具和库提供了丰富的功能来支持各种机器学习任务。 作为一本以示例为基础的学习书籍，《Python Machine Learning By Example Third Edition》适合初学者以及希望加深对机器学习理解的中级读者。通过本书，读者将获得构建和部署实际机器学习模型的能力，并能够在数据科学项目中应用这些技能。"

Python Machine Learning By Example azw3

2018-04-27 上传
Python Machine Learning By Example 英文azw3 本资源转载自网络，如有侵权，请联系上传者或csdn删除

Python_Machine_Learning_By_Example 高清pdf及随书代码

2018-08-23 上传
Python_Machine_Learning_By_Example 原书高清pdf 及随书代码

Python_Machine_Learning_By_Example

2017-08-03 上传
Python_Machine_Learning_By_Example

Python Machine Learning By Example [2017].azw3电子书下载

2017-06-11 上传
Python Machine Learning By Example by Yuxi (Hayden) Liu English | 31 May 2017 | ASIN: B01MT7ATL5 | 254 Pages | AZW3 | 3.86 MB Key Features Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniques Book Description Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. What you will learn Exploit the power of Python to handle data extraction, manipulation, and exploration techniques Use Python to visualize data spread across multiple dimensions and extract useful features Dive deep into the world of analytics to predict situations correctly Implement machine learning classification and regression algorithms from scratch in Python Be amazed to see the algorithms in action Evaluate the performance of a machine learning model and optimize it Solve interesting real-world problems using machine learning and Python as the journey unfolds About the Author Yuxi (Hayden) Liu is currently a data scientist working on messaging app optimization at a multinational online media corporation in Toronto, Canada. He is focusing on social graph mining, social personalization, user demographics and interests prediction, spam detection, and recommendation systems. He has worked for a few years as a data scientist at several programmatic advertising companies, where he applied his machine learning expertise in ad optimization, click-through rate and conversion rate prediction, and click fraud detection. Yuxi earned his degree from the University of Toronto, and published five IEEE transactions and conference papers during his master's research. He finds it enjoyable to crawl data from websites and derive valuable insights. He is also an investment enthusiast. Table of Contents Getting Started with Python and Machine Learning Exploring the 20 newsgroups data set Spam email detection with Naive Bayes News topic classification with Support Vector Machine Click-through prediction with tree-based algorithms Click-through rate prediction with logistic regression Stock prices prediction with regression algorithms Best practices

Python Machine Learning By Example -- Code

2018-03-11 上传
Python Machine Learning By Example -- The easiest way to get into machine learning -- Code

Python Machine Learning By Example epub

2017-10-04 上传
Python Machine Learning By Example 英文epub 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

Python Machine Learning By Example-Packt Publishing(2017).epub

2018-03-11 上传
Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. Chapter 1, Getting Started with Python and Machine Learning, is the starting point for someone who is looking forward to enter the field of ML with Python. You will get familiar with the basics of Python and ML in this chapter and set up the software on your machine. Chapter 2, Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms, explains important concepts such as getting the data, its features, and pre-processing. It also covers the dimension reduction technique, principal component analysis, and the k-nearest neighbors algorithm. Chapter 3, Spam Email Detection with Naive Bayes, covers classification, naive Bayes, and its in-depth implementation, classification performance evaluation, model selection and tuning, and cross-validation. Examples such as spam e-mail detection are demonstrated. Chapter 4, News Topic Classification with Support Vector Machine, covers multiclass classification, Support Vector Machine, and how it is applied in topic classification. Other important concepts, such as kernel machine, overfitting, and regularization, are discussed as well. Chapter 5, Click-Through Prediction with Tree-Based Algorithms, explains decision trees and random forests in depth over the course of solving an advertising click-through rate problem. Chapter 6, Click-Through Prediction with Logistic Regression, explains in depth the logistic regression classifier. Also, concepts such as categorical variable encoding, L1 and L2 regularization, feature selection, online learning, and stochastic gradient descent are detailed. Chapter 7, Stock Price Prediction with Regression Algorithms, analyzes predicting stock market prices using Yahoo/Google Finance data and maybe addit

基于微信小程序的社区门诊管理系统php.zip

2024-12-25 上传
基于Php语言设计并实现了微信小程序的社区门诊管理系统。该小程序基于B/S即所谓浏览器/服务器模式，选择MySQL作为后台数据库去开发并实现一个以微信小程序的社区门诊为核心的系统以及对系统的简易介绍。 用户注册，在用户注册页面通过填写账号、密码、确认密码、姓名、性别、手机、等信息进行注册操作； 用户登录，用户通过登录页面输入账号和密码，并点击登录进行小程序登录操作。 用户登陆微信端后，可以对首页、门诊信息、我的等功能进行详细操作 门诊信息，在门诊信息页面可以查看科室名称、科室类型、医生编号、医生姓名、 职称、坐诊时间、科室图片、点击次数、科室介绍等信息进行预约挂号操作 检查信息，在检查信息页面可以查看检查项目、检查地点、检查时间、检查费用、账号、姓名、医生编号、医生姓名、是否支付、审核回复、审核状态等信息进行支付操作

白色大气风格的设计师作品模板下载.zip

2024-12-25 上传
白色大气风格的设计师作品模板下载.zip

工程经济学自考必备软件下载

2024-12-25 上传
工程经济学自考必备软件下载
LiuTitanium
  • 粉丝: 28
  • 资源: 4684
上传资源 快速赚钱

最新资源