实用机器学习

Mastering Machine Learning With scikit learn pdf

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Mastering_Machine_Learning_with_scikit－learn.pdf.pdf

Mastering_Machine_Learning_with_scikit－learn.pdf

Mastering Machine Learning with scikit-learn

In this book, we will examine several machine learning models and learning algorithms. We will discuss tasks that machine learning is commonly applied to, and learn to measure the performance of machine learning systems. We will work with a popular library for the Python programming language called scikit-learn, which has assembled excellent implementations of many machine learning models and algorithms under a simple yet versatile API.

Mastering Machine Learning With scikit-learn

This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features. You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices.

Mastering Machine Learning With scikit-learn.pdf 中文版

Mastering Machine Learning With scikit-learn（中文文字版）.pdf

Mastering Machine Learning with scikit-learn代码

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[免积分]Mastering Machine Learning with scikit-learn.pdf

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Mastering Machine Learning with scikit-learn [2017,第二版]

Mastering Machine Learning with scikit-learn - Second Edition by Gavin Hackeling English | 24 July 2017 | ASIN: B06ZYRPFMZ | ISBN: 1783988363 | 254 Pages | AZW3 | 5.17 MB Key Features Master popular...

mastering machine learning with scikit-learn 源代码

mastering machine learning with scikit-learn 源代码

Mastering.Machine.Learning.with.R.

Master machine learning techniques with R to deliver insights for complex projects About This Book Get to grips with the application of Machine Learning methods using an extensive set of R packages Understand the benefits and potential pitfalls of using machine learning methods Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML system Who This Book Is For If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful. What You Will Learn Gain deep insights to learn the applications of machine learning tools to the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Familiarize yourself with fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Realize why and how to apply unsupervised learning methods In Detail Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R―a cross-platform, zero-cost statistical programming environment―there has never been a better time to start applying machine learning to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will g

中文Mastering Machine Learning With scikit-learn

Mastering Machine Learning With scikit-learn 中文版

（中文翻译）Mastering Machine Learning with scikit-learn

（中文翻译）Mastering Machine Learning with scikit-learn

Mastering Machine Learning with scikit-learn 2013版

大数据分析工具sckit-learn的教程。

Mastering Machine Learning with scikit-learn(中英文版加源代码)

中英文版Mastering Machine Learning with scikit-learn，另附源码和数据。全网最全本书资料合集！

(英文第二版)Mastering Machine Learning with scikit-learn, 2nd Edition

(英文第二版)Mastering Machine Learning with scikit-learn, 2nd Edition(英文第二版)Mastering Machine Learning with scikit-learn, 2nd Edition

mastering machine learning with scikit learn中文和英文两个版本

mastering machine learning with scikit learn中文和英文两个版本，介绍了sklearn及使用教程，很值得学习

mastering machine learning with scikit-learn

mastering machine learning with scikit-learn

Scikit-learn学习资料荟萃

从入门到精通，学习备查两相宜，资料目录： Mastering Machine Learning with scikit-learn(中文版).pdf； python_scikit-learn学习笔记.pdf； Scikit-learn 使用手册中文版.pdf； scikit-learn-docs.pdf；

Mastering_Machine_Learning_With_scikit－learn中文、英文

Mastering_Machine_Learning_With_scikit－learn 中文、英文版本，高清完整

Mastering Machine Learning with scikit-learn -2017.7.24

Book Description Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. What you will learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the performance of machine learning systems in common tasks About the Author Gavin Hackeling is a data scientist and author. He was worked on a variety of machine learning problems, including automatic speech recognition, document classification, object recognition, and semantic segmentation. An alumnus of the University of North Carolina and New York University, he lives in Brooklyn with his wife and cat. Contents Chapter 1. The Fundamentals of Machine Learning Chapter 2. Simple linear regression Chapter 3. Classification and Regression with K Nearest Neighbors Chapter 4. Feature Extraction and Preprocessing Chapter 5. From Simple Regression to Multiple Regression Chapter 6. From Linear Regression to Logistic Regression Chapter 7. Naive Bayes Chapter 8. Nonlinear Classification and Regression with Decision Trees Chapter 9. From Decision Trees to Random Forests, and other Ensemble Methods Chapter 10. The Perceptron Chapter 11. From the Perceptron to Support Vector Machines Chapter 12. From the Perceptron to Artificial Neural Networks Chapter 13. Clustering with K-Means Chapter 14. Dimensionality Reduction with Principal Component Analysis

Mastering Machine Learning With scikit-learn 中文版

Mastering Machine Learning With scikit-learn 中文版

《Mastering Machine Learning with scikit-learn》源代码

Mastering Machine Learning with scikit-learn 一书的源代码。使用 Python 演示各种机器学习算法。

Mastering Machine Learning with scikit-learn 第二版 2017

Mastering Machine Learning with scikit-learn (2 ed) (True PDF + AWZ3 + codes) Table of Contents Preface 1 Chapter 1: The Fundamentals of Machine Learning 6 Defining machine learning 6 Learning from experience 8 Machine learning tasks 9 Training data, testing data, and validation data 10 Bias and variance 13 An introduction to scikit-learn 15 Installing scikit-learn 16 Installing using pip 17 Installing on Windows 17 Installing on Ubuntu 16.04 17 Installing on Mac OS 17 Installing Anaconda 18 Verifying the installation 18 Installing pandas, Pillow, NLTK, and matplotlib 18 Summary 19 Chapter 2: Simple Linear Regression 20 Simple linear regression 20 Evaluating the fitness of the model with a cost function 25 Solving OLS for simple linear regression 27 Evaluating the model 29 Summary 31 Chapter 3: Classification and Regression with k-Nearest Neighbors 32 K-Nearest Neighbors 32 Lazy learning and non-parametric models 33 Classification with KNN 34 Regression with KNN 42 Scaling features 44 Summary 47 Chapter 4: Feature Extraction 48 Extracting features from categorical variables 48 Standardizing features 49 [ ii ] Extracting features from text 50 The bag-of-words model 50 Stop word filtering 53 Stemming and lemmatization 54 Extending bag-of-words with tf-idf weights 57 Space-efficient feature vectorizing with the hashing trick 59 Word embeddings 61 Extracting features from images 64 Extracting features from pixel intensities 65 Using convolutional neural network activations as features 66 Summary 68 Chapter 5: From Simple Linear Regression to Multiple Linear Regression 70 Multiple linear regression 70 Polynomial regression 74 Regularization 79 Applying linear regression 80 Exploring the data 81 Fitting and evaluating the model 84 Gradient descent 86 Summary 90 Chapter 6: From Linear Regression to Logistic Regression 91 Binary classification with logistic regression 92 Spam filtering 94 Binary classification performance metrics 95 Accuracy 97 Precision and recall 98 Calculating the F1 measure 99 ROC AUC 100 Tuning models with grid search 102 Multi-class classification 104 Multi-class classification performance metrics 107 Multi-label classification and problem transformation 108 Multi-label classification performance metrics 113 Summary 114 Chapter 7: Naive Bayes 115 Bayes' theorem 115 Generative and discriminative models 117 [ iii ] Naive Bayes 118 Assumptions of Naive Bayes 119 Naive Bayes with scikit-learn 120 Summary 124 Chapter 8: Nonlinear Classification and Regression with Decision Trees 125 Decision trees 125 Training decision trees 127 Selecting the questions 128 Information gain 131 Gini impurity 136 Decision trees with scikit-learn 137 Advantages and disadvantages of decision trees 139 Summary 140 Chapter 9: From Decision Trees to Random Forests and Other Ensemble Methods 141 Bagging 141 Boosting 144 Stacking 146 Summary 148 Chapter 10: The Perceptron 149 The perceptron 149 Activation functions 150 The perceptron learning algorithm 152 Binary classification with the perceptron 153 Document classification with the perceptron 161 Limitations of the perceptron 162 Summary 163 Chapter 11: From the Perceptron to Support Vector Machines 164 Kernels and the kernel trick 165 Maximum margin classification and support vectors 169 Classifying characters in scikit-learn 172 Classifying handwritten digits 172 Classifying characters in natural images 175 Summary 177 Chapter 12: From the Perceptron to Artificial Neural Networks 178 Nonlinear decision boundaries 179 [ iv ] Feed-forward and feedback ANNs 180 Multi-layer perceptrons 181 Training multi-layer perceptrons 183 Backpropagation 184 Training a multi-layer perceptron to approximate XOR 189 Training a multi-layer perceptron to classify handwritten digits 192 Summary 193 Chapter 13: K-means 194 Clustering 194 K-means 197 Local optima 203 Selecting K with the elbow method 204 Evaluating clusters 207 Image quantization 209 Clustering to learn features 211 Summary 214 Chapter 14: Dimensionality Reduction with Principal Component Analysis 215 Principal component analysis 215 Variance, covariance, and covariance matrices 220 Eigenvectors and eigenvalues 222 Performing PCA 224 Visualizing high-dimensional data with PCA 227 Face recognition with PCA 228 Summary 231 Index 233

scikit learn Machine Learning Simplified -2017.pdf

Module 3, Mastering Machine Learning with scikit-learn, in this module, we will examine several machine learning models and learning algorithms. We will discuss tasks that machine learning is commonly...

Mastering-Machine-Learning-with-scikit-learn-Second-Edition

Sklearn 书上每章节的代码， python，OpenCV PANDAS, Dataframes，

完整车牌号识别程序，可以识别车牌和颜色，可以集成到项目中 支持win7+

基于.Net开发车牌号识别程序，通过控制台输出结果，可以通过启动参数传入地址，集成到项目中。 使用介绍：https://blog.csdn.net/billyyi/article/details/138597795

ChatGPT教程（终极版）最全整理

这是一篇动了某些人利益的良心教程。 这是一篇姗姗来迟的ChatGPT教程。 纯小白关于ChatGPT入门，你看我这篇文章就够了。 如果你已经用上了ChatGPT，更要恭喜你挖到宝藏，后面的高级技巧一定能让你有收获。 文章包含以下内容： 一、ChatGPT是啥？有什么用； 二、ChatGPT如何注册； 三、ChatGPT使用方法； 四、用ChatGPT搞钱； 五、高级技巧；

博客中Kmeans以及FCM算法数据（免积分）

博客中Kmeans以及FCM算法的数据，包括IRIS鸢尾花数据集、Wine葡萄酒数据集、Seed小麦种子数据集、glass数据集、WDBD乳腺癌数据集，下载在直接存入项目文件夹即可，如果下载不了，可以私信我，看到后会及时回复。