机器学习实战：基于ScikitLearn和TensorFlow

机器学习实战_Machine_Learning_in_Action.pdf

机器学习实战_Machine_Learning_in_Action.pdf

Machine Learning In Action pdf

Machine Learning In Action filetype:pdf 机器学习的python实践

Machine Learning in Action.pdf

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. What's InsideA no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos =================================== Table of ContentsPART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

Machine Learning in Action 原版PDF by Harrington

This book sets out to introduce people to important machine learning algorithms. Tools and applications using these algorithms are introduced to give the reader an idea of how they are used in practice today. A wide selection of machine learning books is available, which discuss the mathematics, but discuss little of how to program the algorithms. This book aims to be a bridge from algorithms presented in matrix form to an actual functioning program. With that in mind, please note that this book is heavy on code and light on mathematics

斯坦福机器学习编程作业machine-learning-ex1，Linear Regression，线性回归

斯坦福机器学习编程作业machine-learning-ex1，Linear Regression，线性回归题目，满分，2015最新作业答案

NTU机器学习源码Machine-learning

Machine-learning机器学习源码

关于机器学习的内容-Machine-Learning.zip

关于机器学习的内容-Machine-Learning

Machine-Learning-From-Scratch常用机器学习的算法简洁实现

Machine-Learning-From-Scratch常用机器学习的算法简洁实现

机器学习算法项目-python-Machine-learning.zip

机器学习算法项目-python-Machine-learning

《Machine Learning in Action》+SourceCode

Peter Harrington,《Machine Learning in Action》,附加源码和数据集。

Mining of Massive Datasets（2nd edition）

大数据-互联网大规模书数据挖掘与分布式处理（第2版）英文版

大数据(Mining of Massive Datasets)

英文PDF版。 《大数据:互联网大规模数据挖掘与分布式处理》由斯坦福大学的“web 挖掘”课程的内容总结而成，主要关注极大规模数据的挖掘。主要内容包括分布式文件系统、相似性搜索、搜索引擎技术、频繁项集挖掘、聚类算法、广告管理及推荐系统。其中相关章节有对应的习题，以巩固所讲解的内容。

Mining of Massive Datasets

The book is based on Stanford Computer Science course CS246: Mining Massive Datasets (and CS345A: Data Mining). The book, like the course, is designed at the undergraduate computer science level with no formal prerequisites. To support deeper explorations, most of the chapters are supplemented with further reading references. The Mining of Massive Datasets book has been published by Cambridge University Press. You can get a 20% discount by applying the code MMDS20 at checkout. By agreement with the publisher, you can download the book for free from this page. Cambridge University Press does, however, retain copyright on the work, and we expect that you will obtain their permission and acknowledge our authorship if you republish parts or all of it. We welcome your feedback on the manuscript.

Mining of Massive Datasets第二版

Mining of Massive Datasets第二版

机实用机器学习Practical-Machine-Learning-master

实用机器学习Practical-Machine-Learning-master

斯坦福机器学习编程作业machine-learning-ex2，Logistic Regression逻辑回归题目，满分，2015最新作业答案

斯坦福机器学习编程作业machine-learning-ex2，Logistic Regression逻辑回归题目，满分，2015最新作业答案，MATLAB

人在回路机器学习Human-in-the-Loop_Machine_Learning.pdf

Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Summary Most machine learning systems that are deployed in the world today learn from human ...

讲解常见的机器学习算法-Machine-Learning.zip

讲解常见的机器学习算法-Machine-Learning

machine-learning-notes机器学习笔记

machine-learning-notes机器学习笔记

Mining of Massive Datasets, 英文原版，斯坦福CS246官方教程

Mining of Massive Datasets, 英文原版，斯坦福CS246官方教程

Mining of Massive Datasets.pdf

This book evolved from material developed over several years by Anand Rajaraman and Jeff Ullman for a one-quarter course at Stanford. The course CS345A, titled “Web Mining,” was designed as an advanced graduate course, although it has become accessible and interesting to advanced undergraduates. When Jure Leskovec joined the Stanford faculty, we reorganized the material considerably. He introduced a new course CS224W on network analysis and added material to CS345A, which was renumbered CS246. The three authors also introduced a large-scale data-mining project course, CS341. The book now contains material taught in all three courses.

基于支持向量机的机器学习研究 Research of Machine-Learning Based Support Vector Machine

learning let computer simulate man’s learning-action,automatically obtain new knowledge and new technique by learn,improve technique,realize own-perfect. To traditional statistics,Statistical ...

机器学习技法6 - 4 - Machine Learning in Action (12-59).mp4

机器学习技法6 - 4 - Machine Learning in Action (12-59).mp4

notes-machine-learning-master_机器学习笔记_机器学习_

机器学习笔记

《Mining of Massive Datasets》

这本书基于两门斯坦福大学计算机科学专业的课程： CS246 和 CS35A。它的受众为计算机专业的本科学生，不需要任何基础（高中数学学好即可）。剑桥大学出版社出版。译本名为《大数据:互联网大规模数据挖掘与分布式处理》。 This book evolved from material developed over several years by Anand Rajaraman and Jeff Ullman for a one-quarter course at Stanford. The course CS345A, titled “Web Mining,” was designed as an advanced graduate course, although it has become accessible and interesting to advanced undergraduates. When Jure Leskovec joined the Stanford faculty, we reorganized the material considerably. He introduced a new course CS224W on network analysis and added material to CS345A, which was renumbered CS246. The three authors also introduced a large-scale data-mining project course, CS341. The book now contains material taught in all three courses.

Mining of Massive Dataset的中文版

Mining of Massive Dataset的中文版