标签归档:R语言

一键收藏自然语言处理学习资源大礼包

虽然知道大多数同学都有资料收藏癖,还是给大家准备一份自然语言处理学习大礼包,其实是之前陆陆续续分享的NLP学习资源,包括自然语言处理、深度学习、机器学习、数学相关的经典课程、书籍和学习笔记,这些资料基本上都是公开渠道可以获得的,整理到一起,方便NLP爱好者收藏把玩。当然,学习的前提依然是”学自然语言处理,其实更应该学好英语“

获取方法很简单,关注AINLP公众号,后台回复关键词:ALL4NLP,一键打包收藏NLP学习资源:

这些自然语言处理相关资源列表如下,欢迎收藏:

相关的资源的过往文章大致介绍如下,不限于下述文章:

斯坦福大学自然语言处理经典入门课程-Dan Jurafsky 和 Chris Manning 教授授课

哥伦比亚大学经典自然语言处理公开课,数学之美中盛赞的柯林斯(Michael Collins)教授授课

认真推荐一份深度学习笔记:简约而不简单

Andrew Ng 老师新推的通俗人工智能课程以及其他相关资料

那些值得推荐和收藏的线性代数学习资源

Philipp Koehn大神的神经网络机器翻译学习资料:NMT Book

凸优化及无约束最优化相关资料

斯坦福大学深度学习自然语言处理课程CS224N 2019 全20个视频分享

自然语言处理经典书籍《Speech and Language Processing》第三版最新版下载(含第二版)

强化学习圣经:《强化学习导论》第二版(附PDF下载)

新书下载 | 面向机器学习的数学(Mathematics for Machine Learning)

Springer面向公众开放正版电子书籍,附65本数学、编程、数据挖掘、数据科学、数据分析、机器学习、深度学习、人工智能相关书籍链接及打包下载

最后,欢迎关注AINLP,回复"all4nlp"获取:

Springer面向公众开放正版电子书籍,附65本数学、编程、数据挖掘、数据科学、数据分析、机器学习、深度学习、人工智能相关书籍链接及打包下载

施普林格(Springer)是世界著名的科技期刊、图书出版公司,这次疫情期间面向公众免费开放了一批社科人文,自然科学等领域的正版电子书籍(据说是400多本),towardsdatascience 上有学者将其中65本机器学习和数据科学以及统计相关的免费教材下载链接整理了出来,我试了一下,无需注册,可以直接下载相关的PDF书籍,相当方便:Springer has released 65 Machine Learning and Data books for free(https://towardsdatascience.com/springer-has-released-65-machine-learning-and-data-books-for-free-961f8181f189)。

看了一下这份书单包括的书籍还是很棒的,包括数学类(多元微积分和几何、计算几何、偏微分、代数、线性代数、线性规划、概率和统计、统计学、统计学习、数学建模等)、编程类(数据结构与算法、Python编程、R语言、编程语言基础、面向对象分析和设计、数据库等)、数据挖掘、数据分析、数据科学、机器学习、人工智能、深度学习、计算机视觉,机器人等相关的电子书,甚至包括如何学习LaTex,远比想象的丰富很多。

这份清单的第一本书籍就是经典的“统计学习基础(ESL,The Elements of Statistical Learning)”, 进入书籍页面后,直接点击“Download PDF” 即可单独下载该书电子版:

Reddit上有网友提供了一个Google Drive的打包下载链接,包括其中64本书籍的PDF打包下载,可以直接下载:

https://www.reddit.com/r/opendirectories/comments/g91u12/google_drive_with_64_books_from_springer_about
https://drive.google.com/drive/folders/1rDJvZsz8EEuVVgZ43pwSvFRRKUo2TIIY

如果还是不方便,可以关注AINLP公众号,回复"sprg"获取百度网盘链接:

这份书籍清单和链接如下,我简单翻译了一下书名,供感兴趣的朋友参考:

The Elements of Statistical Learning(统计学习基础)

Trevor Hastie, Robert Tibshirani, Jerome Friedman

http://link.springer.com/openurl?genre=book&isbn=978-0-387-84858-7

Introductory Time Series with R(时间序列导论-基于R语言讲解)

Paul S.P. Cowpertwait, Andrew V. Metcalfe

http://link.springer.com/openurl?genre=book&isbn=978-0-387-88698-5

A Beginner’s Guide to R(R语言初学者指南)

Alain Zuur, Elena N. Ieno, Erik Meesters

http://link.springer.com/openurl?genre=book&isbn=978-0-387-93837-0

Introduction to Evolutionary Computing(进化计算导论)

A.E. Eiben, J.E. Smith

http://link.springer.com/openurl?genre=book&isbn=978-3-662-44874-8

Data Analysis(数据分析)

Siegmund Brandt

http://link.springer.com/openurl?genre=book&isbn=978-3-319-03762-2

Linear and Nonlinear Programming(线性和非线性规划)

David G. Luenberger, Yinyu Ye

http://link.springer.com/openurl?genre=book&isbn=978-3-319-18842-3

Introduction to Partial Differential Equations(偏微分方程简介)

David Borthwick

http://link.springer.com/openurl?genre=book&isbn=978-3-319-48936-0

Fundamentals of Robotic Mechanical Systems(机器人机械系统基础)

Jorge Angeles

http://link.springer.com/openurl?genre=book&isbn=978-3-319-01851-5

Data Structures and Algorithms with Python(Python数据结构和算法)

Kent D. Lee, Steve Hubbard

http://link.springer.com/openurl?genre=book&isbn=978-3-319-13072-9

Introduction to Partial Differential Equations(偏微分方程简介)

Peter J. Olver

http://link.springer.com/openurl?genre=book&isbn=978-3-319-02099-0

Methods of Mathematical Modelling(数学建模方法)

Thomas Witelski, Mark Bowen

http://link.springer.com/openurl?genre=book&isbn=978-3-319-23042-9

LaTeX in 24 Hours(24小时掌握LaTeX)

Dilip Datta

http://link.springer.com/openurl?genre=book&isbn=978-3-319-47831-9

Introduction to Statistics and Data Analysis(统计与数据分析导论)

Christian Heumann, Michael Schomaker, Shalabh

http://link.springer.com/openurl?genre=book&isbn=978-3-319-46162-5

Principles of Data Mining(数据挖掘原理)

Max Bramer

http://link.springer.com/openurl?genre=book&isbn=978-1-4471-7307-6

Computer Vision(计算机视觉)

Richard Szeliski

http://link.springer.com/openurl?genre=book&isbn=978-1-84882-935-0

Data Mining(数据挖掘)

Charu C. Aggarwal

http://link.springer.com/openurl?genre=book&isbn=978-3-319-14142-8

Computational Geometry(计算几何)

Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars

http://link.springer.com/openurl?genre=book&isbn=978-3-540-77974-2

Robotics, Vision and Control(机器人,视觉与控制)

Peter Corke

http://link.springer.com/openurl?genre=book&isbn=978-3-319-54413-7

Statistical Analysis and Data Display(统计分析和数据展示)

Richard M. Heiberger, Burt Holland

http://link.springer.com/openurl?genre=book&isbn=978-1-4939-2122-5

Statistics and Data Analysis for Financial Engineering(金融工程统计与数据分析)

David Ruppert, David S. Matteson

http://link.springer.com/openurl?genre=book&isbn=978-1-4939-2614-5

Stochastic Processes and Calculus(随机过程与微积分)

Uwe Hassler

http://link.springer.com/openurl?genre=book&isbn=978-3-319-23428-1

Statistical Analysis of Clinical Data on a Pocket Calculator(袖珍计算器上的临床数据统计分析)

Ton J. Cleophas, Aeilko H. Zwinderman

http://link.springer.com/openurl?genre=book&isbn=978-94-007-1211-9

Clinical Data Analysis on a Pocket Calculator(袖珍计算器的临床数据分析)

Ton J. Cleophas, Aeilko H. Zwinderman

http://link.springer.com/openurl?genre=book&isbn=978-3-319-27104-0

The Data Science Design Manual(数据科学设计手册)

Steven S. Skiena

http://link.springer.com/openurl?genre=book&isbn=978-3-319-55444-0

An Introduction to Machine Learning(机器学习导论)

Miroslav Kubat

http://link.springer.com/openurl?genre=book&isbn=978-3-319-63913-0

Guide to Discrete Mathematics(离散数学指南)

Gerard O’Regan

http://link.springer.com/openurl?genre=book&isbn=978-3-319-44561-8

Introduction to Time Series and Forecasting(时间序列和预测简介)

Peter J. Brockwell, Richard A. Davis

http://link.springer.com/openurl?genre=book&isbn=978-3-319-29854-2

Multivariate Calculus and Geometry(多元微积分和几何)

Seán Dineen

http://link.springer.com/openurl?genre=book&isbn=978-1-4471-6419-7

Statistics and Analysis of Scientific Data(科学数据统计与分析)

Massimiliano Bonamente

http://link.springer.com/openurl?genre=book&isbn=978-1-4939-6572-4

Modelling Computing Systems(建模计算系统)

Faron Moller, Georg Struth

http://link.springer.com/openurl?genre=book&isbn=978-1-84800-322-4

Search Methodologies(搜索方法论)

Edmund K. Burke, Graham Kendall

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-6940-7

Linear Algebra Done Right(线性代数应该这样学)

Sheldon Axler

http://link.springer.com/openurl?genre=book&isbn=978-3-319-11080-6

Linear Algebra(线性代数)

Jörg Liesen, Volker Mehrmann

http://link.springer.com/openurl?genre=book&isbn=978-3-319-24346-7

Algebra(代数)

Serge Lang

http://link.springer.com/openurl?genre=book&isbn=978-1-4613-0041-0

Understanding Analysis(理解分析学)

Stephen Abbott

http://link.springer.com/openurl?genre=book&isbn=978-1-4939-2712-8

Linear Programming(线性规划)

Robert J Vanderbei

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-7630-6

Understanding Statistics Using R(通过R语言学习统计学)

Randall Schumacker, Sara Tomek

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-6227-9

An Introduction to Statistical Learning(统计学习导论)

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-7138-7

Statistical Learning from a Regression Perspective(回归视角的统计学习)

Richard A. Berk

http://link.springer.com/openurl?genre=book&isbn=978-3-319-44048-4

Applied Partial Differential Equations(应用偏微分方程)

J. David Logan

http://link.springer.com/openurl?genre=book&isbn=978-3-319-12493-3

Robotics(机器人技术)

Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo

http://link.springer.com/openurl?genre=book&isbn=978-1-84628-642-1

Regression Modeling Strategies(回归建模策略)

Frank E. Harrell , Jr.

http://link.springer.com/openurl?genre=book&isbn=978-3-319-19425-7

A Modern Introduction to Probability and Statistics(概率统计的现代视角导论)

F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester

http://link.springer.com/openurl?genre=book&isbn=978-1-84628-168-6

The Python Workbook(Python手册)

Ben Stephenson

http://link.springer.com/openurl?genre=book&isbn=978-3-319-14240-1

Machine Learning in Medicine — a Complete Overview(医学中的机器学习-完整概述)

Ton J. Cleophas, Aeilko H. Zwinderman

http://link.springer.com/openurl?genre=book&isbn=978-3-319-15195-3

Object-Oriented Analysis, Design and Implementation(面向对象的分析,设计与实现)

Brahma Dathan, Sarnath Ramnath

http://link.springer.com/openurl?genre=book&isbn=978-3-319-24280-4

Introduction to Data Science(数据科学导论)

Laura Igual, Santi Seguí

http://link.springer.com/openurl?genre=book&isbn=978-3-319-50017-1

Applied Predictive Modeling(应用预测建模)

Max Kuhn, Kjell Johnson

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-6849-3

Python For ArcGIS(面向ArcGIS的Python指南)

Laura Tateosian

http://link.springer.com/openurl?genre=book&isbn=978-3-319-18398-5

Concise Guide to Databases(简明数据库指南)

Peter Lake, Paul Crowther

http://link.springer.com/openurl?genre=book&isbn=978-1-4471-5601-7

Digital Image Processing(数字图像处理)

Wilhelm Burger, Mark J. Burge

http://link.springer.com/openurl?genre=book&isbn=978-1-4471-6684-9

Bayesian Essentials with R(通过R学习贝叶斯基础)

Jean-Michel Marin, Christian P. Robert

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-8687-9

Robotics, Vision and Control(机器人,视觉与控制)

Peter Corke

http://link.springer.com/openurl?genre=book&isbn=978-3-642-20144-8

Foundations of Programming Languages(编程语言基础)

Kent D. Lee

http://link.springer.com/openurl?genre=book&isbn=978-3-319-70790-7

Introduction to Artificial Intelligence(人工智能导论)

Wolfgang Ertel

http://link.springer.com/openurl?genre=book&isbn=978-3-319-58487-4

Introduction to Deep Learning(深度学习导论)

Sandro Skansi

http://link.springer.com/openurl?genre=book&isbn=978-3-319-73004-2

Linear Algebra and Analytic Geometry for Physical Sciences(物理科学的线性代数和解析几何)

Giovanni Landi, Alessandro Zampini

http://link.springer.com/openurl?genre=book&isbn=978-3-319-78361-1

Applied Linear Algebra(应用线性代数)

Peter J. Olver, Chehrzad Shakiban

http://link.springer.com/openurl?genre=book&isbn=978-3-319-91041-3

Neural Networks and Deep Learning(神经网络与深度学习)

Charu C. Aggarwal

http://link.springer.com/openurl?genre=book&isbn=978-3-319-94463-0

Data Science and Predictive Analytics(数据科学与预测分析)

Ivo D. Dinov

http://link.springer.com/openurl?genre=book&isbn=978-3-319-72347-1

Analysis for Computer Scientists(面向计算机科学家的分析学)

Michael Oberguggenberger, Alexander Ostermann

http://link.springer.com/openurl?genre=book&isbn=978-3-319-91155-7

Excel Data Analysis(Excel数据分析)

Hector Guerrero

http://link.springer.com/openurl?genre=book&isbn=978-3-030-01279-3

A Beginners Guide to Python 3 Programming(Python 3编程入门指南)

John Hunt

http://link.springer.com/openurl?genre=book&isbn=978-3-030-20290-3

Advanced Guide to Python 3 Programming(Python 3编程高级指南)

John Hunt

http://link.springer.com/openurl?genre=book&isbn=978-3-030-25943-3

感兴趣的同学可以关注下方公众号,回复"sprg"获取打包下载网盘链接:

Coursera上数据科学相关课程(公开课)汇总推荐

Coursera上的数据科学课程有很多,这里汇总一批。

1、 Introduction to Data Science Specialization

IBM公司推出的数据科学导论专项课程系列(Introduction to Data Science Specialization),这个系列包括4门子课程,涵盖数据科学简介,面向数据科学的开源工具,数据科学方法论,SQL基础,感兴趣的同学可以关注:Launch your career in Data Science。Data Science skills to prepare for a career or further advanced learning in Data Science.

1) What is Data Science?
2) Open Source tools for Data Science
3) Data Science Methodology
4) Databases and SQL for Data Science

2、Applied Data Science Specialization

IBM公司推出的 应用数据科学专项课程系列(Applied Data Science Specialization),这个系列包括4门子课程,涵盖面向数据科学的Python,Python数据可视化,Python数据分析,数据科学应用毕业项目,感兴趣的同学可以关注:Get hands-on skills for a Career in Data Science。Learn Python, analyze and visualize data. Apply your skills to data science and machine learning.

1) Python for Data Science
2) Data Visualization with Python
3) Data Analysis with Python
4) Applied Data Science Capstone

3、Applied Data Science with Python Specialization

密歇根大学的Python数据科学应用专项课程系列(Applied Data Science with Python),这个系列的目标主要是通过Python编程语言介绍数据科学的相关领域,包括应用统计学,机器学习,信息可视化,文本分析和社交网络分析等知识,并结合一些流行的Python工具包进行讲授,例如pandas, matplotlib, scikit-learn, nltk以及networkx等Python工具。感兴趣的同学可以关注:Gain new insights into your data-Learn to apply data science methods and techniques, and acquire analysis skills.

1) Introduction to Data Science in Python
2) Applied Plotting, Charting & Data Representation in Python
3) Applied Machine Learning in Python
4) Applied Text Mining in Python
5) Applied Social Network Analysis in Python

4、Data Science Specialization

约翰霍普金斯大学的数据科学专项课程系列(Data Science Specialization),这个系列课程有10门子课程,包括数据科学家的工具箱,R语言编程,数据清洗和获取,数据分析初探,可重复研究,统计推断,回归模型,机器学习实践,数据产品开发,数据科学毕业项目,感兴趣的同学可以关注: Launch Your Career in Data Science-A nine-course introduction to data science, developed and taught by leading professors.

1) The Data Scientist’s Toolbox
2) R Programming
3) Getting and Cleaning Data
4) Exploratory Data Analysis
5) Reproducible Research
6) Statistical Inference
7) Regression Models
8) Practical Machine Learning
9) Developing Data Products
10) Data Science Capstone

5、Data Science at Scale Specialization

华盛顿大学的大规模数据科学专项课程系列(Data Science at Scale ),这个系列包括3门子课程和1个毕业项目课程,包括大规模数据系统和算法,数据分析模型与方法,数据科学结果分析等,感兴趣的同学可以关注: Tackle Real Data Challenges-Master computational, statistical, and informational data science in three courses.

1) Data Manipulation at Scale: Systems and Algorithms
2) Practical Predictive Analytics: Models and Methods
3) Communicating Data Science Results
4) Data Science at Scale – Capstone Project

6、Advanced Data Science with IBM Specialization

IBM公司推出的高级数据科学专项课程系列(Advanced Data Science with IBM Specialization),这个系列包括4门子课程,涵盖数据科学基础,高级机器学习和信号处理,结合深度学习的人工智能应用等,感兴趣的同学可以关注:Expert in DataScience, Machine Learning and AI。Become an IBM-approved Expert in Data Science, Machine Learning and Artificial Intelligence.

1) Fundamentals of Scalable Data Science
2) Advanced Machine Learning and Signal Processing
3) Applied AI with DeepLearning
4) Advanced Data Science Capstone

7、Data Mining Specialization

伊利诺伊大学香槟分校的数据挖掘专项课程系列(Data Mining Specialization),这个系列包含5门子课程和1个毕业项目课程,涵盖数据可视化,信息检索,文本挖掘与分析,模式发现和聚类分析等,感兴趣的同学可以关注:Data Mining Specialization-Analyze Text, Discover Patterns, Visualize Data. Solve real-world data mining challenges.

1) Data Visualization
2) Text Retrieval and Search Engines
3) Text Mining and Analytics
4) Pattern Discovery in Data Mining
5) Cluster Analysis in Data Mining
6) Data Mining Project

8、Data Analysis and Interpretation Specialization

数据分析和解读专项课程系列(Data Analysis and Interpretation Specialization),该系列包括5门子课程,分别是数据管理和可视化,数据分析工具,回归模型,机器学习,毕业项目,感兴趣的同学可以关注:Learn Data Science Fundamentals-Drive real world impact with a four-course introduction to data science.

1) Data Management and Visualization
2) Data Analysis Tools
3) Regression Modeling in Practice
4) Machine Learning for Data Analysis
5) Data Analysis and Interpretation Capstone

9、Executive Data Science Specialization

可管理的数据科学专项课程系列(Executive Data Science Specialization),这个系列包含4门子课程和1门毕业项目课程,涵盖数据科学速成,数据科学小组建设,数据分析管理,现实生活中的数据科学等,感兴趣的同学可以关注:Be The Leader Your Data Team Needs-Learn to lead a data science team that generates first-rate analyses in four courses.

1)A Crash Course in Data Science
2)Building a Data Science Team
3)Managing Data Analysis
4)Data Science in Real Life
5)Executive Data Science Capstone

10、其他相关的数据科学课程

1) Data Science Math Skills
2) Data Science Ethics
3) How to Win a Data Science Competition: Learn from Top Kagglers

注:本文首发“课程图谱博客”:http://blog.coursegraph.com

同步发布到这里, 本本文链接地址:http://blog.coursegraph.com/coursera上数据科学相关课程数据科学公开课汇总推荐 http://blog.coursegraph.com/?p=851