实用机器学习

Python_Machine_Learning_By_Example

Python_Machine_Learning_By_Example

Python Machine Learning By Example -- Code

Python Machine Learning By Example -- The easiest way to get into machine learning -- Code

Python-Machine-Learning-By-Example-Third-Edition:Packt发行的Python Machine Learning By Example Third Edition

Python机器学习以示例为例的第三版 Packt发行的Python Machine Learning By Example Third Edition 作者： （Hayden）Liu（ ） 关于这本书 第三版Python Machine Learning By Example配备了最新的更新，它为ML爱好者提供了全面的课程，以增强他们对ML概念，技术和算法的掌握。

Python Machine Learning By Example epub

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 azw3

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

Python_Machine_Learning_By_Example 原书高清pdf 及随书代码

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

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-Packt Publishing(2017).epub

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

Python Machine Learning

Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics

Python Machine Learning. Machine Learning and Deep Learning with Py, scikit

that will help you get started in machine learning by implementing powerful learning algorithms. Getting exposed to practical code examples and working through example applications of machine learning...

python-machine-learning-by-example:Python Machine Learning by Example习题集

python机器学习示例 Python Machine Learning by Example习题集

Python Machine Learning, 2nd Edition-Packt Publishing(2017).pdf )

that will help you get started in machine learning by implementing powerful learning algorithms. Getting exposed to practical code examples and working through example applications of machine ...

Python Deep Learning - Valentino Zocca

Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects...

Python Machine Learning Blueprints

Key Features, Put machine learning principles into practice to solve real-world problemsGet to grips with Python's impressive range of Machine Learning libraries and frameworksFrom retrieving data ...

Machine-Learning-with-Python

Python机器学习 直方图 密度点 盒子和胡须 相关矩阵 散点图矩阵 重新缩放数据 标准化数据 规范化数据 二进制化数据 单变量选择 递归特征消除 主成分分析 功能重要性 训练和测试集 交叉验证 留出一个交叉验证 重复随机测试训练拆分 1.分类指标 分类精度 对数损失 ROC曲线下的面积 混淆矩阵 分类报告 2.回归指标 平均绝对误差 均方误差 R ^ 2

Python-for-Machine-Learning

Python-for-Machine-Learning

Machine-Learning-in-Python

Machine-Learning-in-Python

Machine-Learning-With-Python

Machine-Learning-With-Python

源码Python Machine Learning. Machine Learning and Deep Learning with Py, scikit

that will help you get started in machine learning by implementing powerful learning algorithms. Getting exposed to practical code examples and working through example applications of machine learning...

Python Machine Learning Blueprints[July 2016]

Chapter 1, The Python Machine Learning Ecosystem, delves into Python, which has a deep and active developer community, and many of these developers come from the scientific community as well....

Statistics for Machine Learning

Master the statistical aspect of machine learning with the help of this example-rich guide in R & Python. Book Description Complex statistics in machine learning worries a lot of developers. Knowing ...

Python.Machine.Learning

学习python机器学习很好的教材资源

Machine-Learning-Python

Machine-Learning-Python

Machine_Learning_with_Python

Machine_Learning_with_Python

R Machine Learning By Example

R Machine Learning By Example Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to make machine learning give them datadriven insights to grow their businesses. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.