标签归档:Coursera公开课

Coursera上博弈论相关课程(公开课)汇总推荐

Deep Learning Specialization on Coursera

博弈论(Game Theory)很有意思,大家可能首先想到的就是赌博,据说博弈论最早源于赌博策略和数学,下面是来自维基百科的解释:

博弈论(英语:game theory),又译为对策论,或者赛局理论,应用数学的一个分支,1944年冯·诺伊曼与奥斯卡·摩根斯特恩合著《博弈论与经济行为》,标志着现代系统博弈理论的的初步形成,因此他被称为“博弈论之父”。博弈论被认为是20世纪经济学最伟大的成果之一。目前在生物学、经济学、国际关系、计算机科学、政治学、军事战略和其他很多学科都有广泛的应用。主要研究公式化了的激励结构(游戏或者博弈)间的相互作用。是研究具有斗争或竞争性质现象的数学理论和方法。也是运筹学的一个重要学科。

作为互联网广告研发人员,应该或多或少了解一点计算广告学,其中支撑Google, 百度等互联网巨头广告业务的竞价排名机制的核心之一就是博弈论。另外经济学中有很多博弈论的影子,电影“美丽心灵”中的主角数学家约翰纳什,由于他与另外两位数学家在非合作博弈的均衡分析理论方面做出了开创性的贡献,对博弈论和经济学产生了重大影响,而获得1994年诺贝尔经济学奖,纳什均衡则是博弈论课程中不可或缺的一节课。Coursera上有好几门博弈论(Game Theory)相关的课程,这里做个汇总整理。

1. 斯坦福大学的 博弈论(Game Theory)

这门课程早在Coursera诞生之初就有了,后经多次优化,现在有上和下两个部分,这门课程属于博弈论上,重在博弈论基础,需要学习者有一定的数学思维和数学基础,例如基础的概率理论和一些微积分基础知识:

This course is aimed at students, researchers, and practitioners who wish to understand more about strategic interactions. You must be comfortable with mathematical thinking and rigorous arguments. Relatively little specific math is required; but you should be familiar with basic probability theory (for example, you should know what a conditional probability is), and some very light calculus would be helpful.

2. 斯坦福大学的 博弈论二: 高级应用(Game Theory II: Advanced Applications)

上门博弈论课程的续集,关注博弈论的应用,包括机制设计,拍卖机制等:

Popularized by movies such as "A Beautiful Mind", game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Over four weeks of lectures, this advanced course considers how to design interactions between agents in order to achieve good social outcomes. Three main topics are covered: social choice theory (i.e., collective decision making and voting systems), mechanism design, and auctions. In the first week we consider the problem of aggregating different agents' preferences, discussing voting rules and the challenges faced in collective decision making. We present some of the most important theoretical results in the area: notably, Arrow's Theorem, which proves that there is no "perfect" voting system, and also the Gibbard-Satterthwaite and Muller-Satterthwaite Theorems. We move on to consider the problem of making collective decisions when agents are self interested and can strategically misreport their preferences. We explain "mechanism design" -- a broad framework for designing interactions between self-interested agents -- and give some key theoretical results. Our third week focuses on the problem of designing mechanisms to maximize aggregate happiness across agents, and presents the powerful family of Vickrey-Clarke-Groves mechanisms. The course wraps up with a fourth week that considers the problem of allocating scarce resources among self-interested agents, and that provides an introduction to auction theory.

3. 东京大学的 博弈论入门课程(Welcome to Game Theory)

入门级博弈论课程,由东京大学推出,英文授课:

This course provides a brief introduction to game theory. Our main goal is to understand the basic ideas behind the key concepts in game theory, such as equilibrium, rationality, and cooperation. The course uses very little mathematics, and it is ideal for those who are looking for a conceptual introduction to game theory. Business competition, political campaigns, the struggle for existence by animals and plants, and so on, can all be regarded as a kind of “game,” in which individuals try to do their best against others. Game theory provides a general framework to describe and analyze how individuals behave in such “strategic” situations. This course focuses on the key concepts in game theory, and attempts to outline the informal basic ideas that are often hidden behind mathematical definitions. Game theory has been applied to a number of disciplines, including economics, political science, psychology, sociology, biology, and computer science. Therefore, a warm welcome is extended to audiences from all fields who are interested in what game theory is all about.

4. 佐治亚理工学院的 组合博弈论(Games without Chance: Combinatorial Game Theory)

这门课程主要关注组合博弈论,覆盖不靠运气游戏背后的数学理论和分析:This course will cover the mathematical theory and analysis of simple games without chance moves.

本课程将讲解如何运用数学理论,分析不含运气步骤(随机步骤)的简单游戏。本课程将探索不含运气步骤(随机步骤)的两个玩家游戏中的数学理论。我们将讨论如何简化游戏,什么情况下游戏等同于数字运算,以及怎样的游戏才算公正。许多例子都是有关一此简单的游戏,有的你可能还没有听说过:Hackenbush(“无向图删边”游戏)、Nim(“拈”游戏)、Push(推箱子游戏)、Toads and Frogs(“蟾蜍和青蛙”游戏),等。虽然完成这门课程并不能让你成为国际象棋或围棋高手,但是会让你更深入了解游戏的结构。

5. 国立台湾大学的 实验经济学: 行为博弈论 (Experimental Economics I: Behavioral Game Theory)

台湾大学王道一副教授 (Associate Professor)的实验经济学课程-行为博弈论:

人是否会如同理论经济学的预测进行决策?这门课将透过每周的课程视频以及课后作业带你了解实验经济学的基本概念。每周将会有习题练习以及指定阅读的期刊论文。你将会参与一些在线的实验、报告论文并且互评其他同学的报告。❖课程介绍(About the course)这是一门进阶的经济学课程,课程目标为介绍实验经济学的基本概念,并且让学生们能开始在这个领域从事自己的相关研究。详细课程目标如下:1.实验经济学的介绍:在上完这堂课之后,学生应能列举经济学各个领域的数个知名实验,并且解释实验结果如何验证或否证经济理论及其他实地数据。2.评论近期相关领域研究:上完这堂课之后,学生应能阅读并评论实验经济学相关的期刊论文。在课堂中,学生将会阅读指定的期刊论文,并且(在视频中)亲自上台报告一篇论文。❖授课形式(Course format)1.本堂课将以视频的形式为主,搭配课后作业的形式来进行。每个同学将阅读一篇实验经济学论文,并录像成两段各10分钟的介绍视频并后上传至Coursera(或上传到Youku,再复制连接到作业上传区)。第一段期中报告视频请同学介绍该论文所描述的实验设计,第二段,也就是期末报告视频则介绍实验结果。此外每位同学至少需观看其他两位同学的呈现内容,并给予评论。2.这堂课将简单地运用以下赛局(博弈)概念:奈许均衡/纳什均衡(Nash Equilibrium)混合策略均衡(Mixed Strategy Equilibrium)子赛局完美均衡/子博弈精练纳什均衡(SPNE)共识/共同知识(Common Knowledge)信念(Belief)

注:本文首发“课程图谱博客”:http://blog.coursegraph.com
同步发布到这里, 本文链接地址:http://blog.coursegraph.com/coursera上博弈论课程博弈论公开课汇总推荐 http://blog.coursegraph.com/?p=782

Coursera上数据结构 & 算法课程(公开课)汇总推荐

Deep Learning Specialization on Coursera

数据结构和算法是基本功,Coursera上有很多数据结构和算法方面的经典课程,这里做个总结。

1. 普林斯顿大学 Sedgewick 教授的 算法1: Algorithms, Part I

这门算法课程已经开过很多轮,好评如潮 ,应该算得上是 Coursera 上的明星算法课程了,感兴趣的同学可以参考课程图谱上的旧版 课程评论,强烈推荐:

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

2. 普林斯顿大学 Sedgewick 教授的 算法2: Algorithms, Part II

系列课程,依然强烈推荐,感兴趣的同学可以参考早期课程的评价:http://coursegraph.com/coursera_algs4partII

“Part II较Part I在部分Programming Assignments上增加了timing和memory的难度,API100%不再意味着全部100%,这正是这门课程的精华之处:不是灌输算法知识,而是通过实际操作的过程让学员深入理解数据结构和算法调优在经济上的意义。个人很喜欢论坛上大家在Performance Thread里贴出自己的report然后交流优化心得的过程,很有圆桌会议的架势。这门课的教授Robert Sedgewick师出名门,是Knuth在斯坦福的博士。老爷子年岁已近70,一直活跃在论坛上解答和讨论问题,敬业程度让人赞叹。”

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

3. 斯坦福大学的 算法专项课程(Algorithms Specialization)

斯坦福大学的算法专项课程系列(Algorithms Specialization),这个系列包含4门子课程,涵盖基础的算法主题和高级算法主题,此前评价非常高,五颗星推荐,感兴趣的同学可以关注: Learn To Think Like A Computer Scientist-Master the fundamentals of the design and analysis of algorithms.

Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists. About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.

可参考老版课程评论:Algorithms: Design and Analysis, Part 1Algorithms: Design and Analysis, Part 2

3.1 Divide and Conquer, Sorting and Searching, and Randomized Algorithms

The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).

3.2 Graph Search, Shortest Paths, and Data Structures

The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

3.3 Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

3.4 Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).

4. 北京大学的 程序设计与算法专项课程系列

据说是国内学生选择最多的中文程序设计课程,这个系列包含7门子课程,分别是计算导论与C语言基础, C程序设计进阶 ,C++程序设计, 算法基础, 数据结构基础, 高级数据结构与算法, 程序开发项目实践,最后一个项目实践课程联合腾讯公司设计一个实际的应用问题:搜索引擎设计。感兴趣的同学可以关注:

本专项课程旨在系统培养你的程序设计与编写能力。系列课程从计算机的基础知识讲起,无论你来自任何学科和行业背景,都能快速理解;同时我们又系统性地介绍了C程序设计,C++程序设计,算法基础,数据结构与算法相关的内容,各门课之间联系紧密,循序渐进,能够帮你奠定坚实的程序开发基础;课程全部配套在线编程测试,将有效地训练和提升你编写程序的实际动手能力。并通过结业实践项目为你提供应用程序设计解决复杂现实问题的锻炼,从而积累实际开发的经验。因此,我们希望本专项课程能够帮助你完成从仅了解基本的计算机知识到能够利用高质量的程序解决实际问题的转变。

5. 加州大学圣地亚哥分校的 数据结构与算法专项课程系列(Data Structures and Algorithms Specialization)

这个系列包含5门子课程和1门毕业项目课程,包括算法工具箱,数据结构 ,图算法,字符串算法 ,高级算法与算法复杂度,算法毕业项目 等,感兴趣的同学可以关注: Master Algorithmic Programming Techniques-Learn algorithms through programming and advance your software engineering or data science career

This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.

注:本文首发“课程图谱博客”:http://blog.coursegraph.com ,同步发布到这里, 本文链接地址:http://blog.coursegraph.com/coursera上数据结构-算法课程-算法公开课-汇总推荐 http://blog.coursegraph.com/?p=736

Coursera上机器学习课程(公开课)汇总推荐

Deep Learning Specialization on Coursera

Coursera上有很多机器学习课程,这里做个总结,因为机器学习相关的概念和应用很多,这里推荐的课程仅限于和机器学习直接相关的课程,虽然深度学习属于机器学习范畴,这里暂时也将其排除在外,后续会专门推出深度学习课程的系列推荐。

1. Andrew Ng 老师的 机器学习课程(Machine Learning)

机器学习入门首选课程,没有之一。这门课程从一开始诞生就备受瞩目,据说全世界有数百万人通过这门课程入门机器学习。课程的级别是入门级别的,对学习者的背景要求不高,Andrew Ng 老师讲解的又很通俗易懂,所以强烈推荐从这门课程开始走入机器学习。课程简介:

机器学习是一门研究在非特定编程条件下让计算机采取行动的学科。最近二十年,机器学习为我们带来了自动驾驶汽车、实用的语音识别、高效的网络搜索,让我们对人类基因的解读能力大大提高。当今机器学习技术已经非常普遍,您很可能在毫无察觉情况下每天使用几十次。许多研究者还认为机器学习是人工智能(AI)取得进展的最有效途径。在本课程中,您将学习最高效的机器学习技术,了解如何使用这些技术,并自己动手实践这些技术。更重要的是,您将不仅将学习理论知识,还将学习如何实践,如何快速使用强大的技术来解决新问题。最后,您将了解在硅谷企业如何在机器学习和AI领域进行创新。 本课程将广泛介绍机器学习、数据挖掘和统计模式识别。相关主题包括:(i) 监督式学习(参数和非参数算法、支持向量机、核函数和神经网络)。(ii) 无监督学习(集群、降维、推荐系统和深度学习)。(iii) 机器学习实例(偏见/方差理论;机器学习和AI领域的创新)。课程将引用很多案例和应用,您还需要学习如何在不同领域应用学习算法,例如智能机器人(感知和控制)、文本理解(网络搜索和垃圾邮件过滤)、计算机视觉、医学信息学、音频、数据库挖掘等领域。

这里有老版课程评论,非常值得参考推荐:Machine Learning

2. 台湾大学林轩田老师的 機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations

如果有一定的基础或者学完了Andrew Ng老师的机器学习课程,这门机器学习基石上-数学基础可以作为进阶课程。林老师早期推出的两门机器学习课程口碑和难度均有:机器学习基石机器学习技法 ,现在重组为上和下,非常值得期待:

Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. This first course of the two would focus more on mathematical tools, and the other course would focus more on algorithmic tools. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。我們的兩項姊妹課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。本課程將較為著重數學類的工具,而另一課程將較為著重方法類的工具。]

3. 台湾大学林轩田老师的 機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations

作为2的姊妹篇,这个机器学习基石下-算法基础 更注重机器学习算法相关知识:

Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. This second course of the two would focus more on algorithmic tools, and the other course would focus more on mathematical tools. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。我們的兩項姊妹課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。本課程將較為著重方法類的工具,而另一課程將較為著重數學類的工具。

可参考早期的老版本课程评论:機器學習基石 (Machine Learning Foundations) 機器學習技法 (Machine Learning Techniques)

4. 华盛顿大学的 "机器学习专项课程(Machine Learning Specialization)"

这个系列课程包含4门子课程,分别是 机器学习基础:案例研究 , 机器学习:回归 , 机器学习:分类, 机器学习:聚类与检索:

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

4.1 Machine Learning Foundations: A Case Study Approach(机器学习基础: 案例研究)

你是否好奇数据可以告诉你什么?你是否想在关于机器学习促进商业的核心方式上有深层次的理解?你是否想能同专家们讨论关于回归,分类,深度学习以及推荐系统的一切?在这门课上,你将会通过一系列实际案例学习来获取实践经历。

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.

4.2 Machine Learning: Regression(机器学习: 回归问题)

这门课程关注机器学习里面的一个基本问题: 回归(Regression), 也通过案例研究(预测房价)的方式进行回归问题的学习,最终通过Python实现相关的机器学习算法。

Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python.

4.3 Machine Learning: Classification(机器学习:分类问题)

这门课程关注机器学习里面的另一个基本问题: 分类(Classification), 通过两个案例研究进行学习:情感分析和贷款违约预测,最终通过Python实现相关的算法(也可以选择其他语言,但是强烈推荐Python)。

Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended).

4.4 Machine Learning: Clustering & Retrieval(机器学习:聚类和检索)

这门课程关注的是机器学习里面的另外两个基本问题:聚类和检索,同样通过案例研究进行学习:相似文档查询,一个非常具有实际应用价值的问题:

Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python.

5. 密歇根大学的 Applied Machine Learning in Python(在Python中应用机器学习)

Python机器学习应用课程,这门课程主要聚焦在通过Python应用机器学习,包括机器学习和统计学的区别,机器学习工具包scikit-learn的介绍,有监督学习和无监督学习,数据泛化问题(例如交叉验证和过拟合)等。这门课程同时属于"Python数据科学应用专项课程系列(Applied Data Science with Python Specialization)"。

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

6. 俄罗斯国立高等经济学院和Yandex联合推出的 高级机器学习专项课程系列(Advanced Machine Learning Specialization)

该系列授课语言为英语,包括深度学习,Kaggle数据科学竞赛,机器学习中的贝叶斯方法,强化学习,计算机视觉,自然语言处理等7门子课程,截止目前前3门课程已开,感兴趣的同学可以关注:

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

以下是和机器学习直接相关的子课程,其他这里略过:

6.3 Bayesian Methods for Machine Learning(面向机器学习的贝叶斯方法)

该课程关注机器学习中的贝叶斯方法,贝叶斯方法在很多领域都很有用,例如游戏开发和毒品发现。它们给很多机器学习算法赋予了“超能力”,例如处理缺失数据,从小数据集中提取大量有用的信息等。当贝叶斯方法被应用在深度学习中时,它可以让你将模型压缩100倍,并且自动帮你调参,节省你的时间和金钱。

Bayesian methods are used in lots of fields: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a really desirable feature for fields like medicine. When Bayesian methods are applied to deep learning, it turns out that they allow you to compress your models 100 folds, and automatically tune hyperparametrs, saving your time and money. In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can fully automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods.

7. 约翰霍普金斯大学的 Practical Machine Learning(机器学习实战)

这门课程从数据科学的角度来应用机器学习进修实战,课程将会介绍机器学习的基础概念譬如训练集,测试集,过拟合和错误率等,同时这门课程也会介绍机器学习的基本模型和算法,例如回归,分类,朴素贝叶斯,以及随机森林。这门课程最终会覆盖一个完整的机器学习实战周期,包括数据采集,特征生成,机器学习算法应用以及结果评估等。这门机器学习实践课程同时属于约翰霍普金斯大学的 数据科学专项课程(Data Science Specialization)系列:

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

8. 卫斯理大学 Regression Modeling in Practice(回归模型实战)

这门课程关注的是数据分析以及机器学习领域的最重要的一个概念和工具:回归(模型)分析。这门课程使用SAS或者Python,从线性回归开始学习,到了解整个回归模型,以及应用回归模型进行数据分析:

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

这门课程同时属于卫斯理大学的 数据分析与解读专项课程系列(Data Analysis and Interpretation Specialization)

9. 卫斯理大学的 Machine Learning for Data Analysis(面向数据分析的机器学习)

这门课程关注数据分析里的机器学习,机器学习的过程是一个开发、测试和应用预测算法来实现目标的过程,这门课程以 Regression Modeling in Practice(回归模型实战) 为基础,介绍机器学习中的有监督学习概念,同时从基础的分类算法到决策树以及聚类都会覆盖。通过完成这门课程,你将会学习如何应用、测试和解读机器学习算法用来解决实际问题。

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions.

这门课程同时属于卫斯理大学的 数据分析与解读专项课程系列(Data Analysis and Interpretation Specialization)

10. 加州大学圣地亚哥分校的 Machine Learning With Big Data(大数据机器学习)

这门课程关注大数据中的机器学习技术,将会介绍相关的机器学习算法和工具。通过这门课程,你可以学到:通过机器学习过程来设计和利用数据;将机器学习技术用于探索和准备数据来建模;识别机器学习问题的类型;通过广泛可用的开源工具来使用数据构建模型;在Spark中使用大规模机器学习算法分析大数据。

Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems. At the end of the course, you will be able to: • Design an approach to leverage data using the steps in the machine learning process. • Apply machine learning techniques to explore and prepare data for modeling. • Identify the type of machine learning problem in order to apply the appropriate set of techniques. • Construct models that learn from data using widely available open source tools. • Analyze big data problems using scalable machine learning algorithms on Spark.

这门课程同时属于 加州大学圣地亚哥分校的大数据专项课程系列(Big Data Specialization)

11. 俄罗斯搜索巨头Yandex推出的 Big Data Applications: Machine Learning at Scale(大数据应用:大规模机器学习)

机器学习正在改变世界,通过这门课程,你将会学习到:识别实战中需要用机器学习算法解决的问题;通过Spark MLLib构建、调参、和应用线性模型;里面文本处理的方法;用决策树和Boost方法解决机器学习问题;构建自己的推荐系统。

Machine learning is transforming the world around us. To become successful, you’d better know what kinds of problems can be solved with machine learning, and how they can be solved. Don’t know where to start? The answer is one button away. During this course you will: - Identify practical problems which can be solved with machine learning - Build, tune and apply linear models with Spark MLLib - Understand methods of text processing - Fit decision trees and boost them with ensemble learning - Construct your own recommender system. As a practical assignment, you will - build and apply linear models for classification and regression tasks; - learn how to work with texts; - automatically construct decision trees and improve their performance with ensemble learning; - finally, you will build your own recommender system! With these skills, you will be able to tackle many practical machine learning tasks. We provide the tools, you choose the place of application to make this world of machines more intelligent.

这门课程同时属于Yandex推出的 面向数据工程师的大数据专项课程系列(Big Data for Data Engineers Specialization)

注:本文首发“课程图谱博客”:http://blog.coursegraph.com ,同步发布到这里, 本文链接地址:http://blog.coursegraph.com/coursera上机器学习课程公开课汇总推荐 http://blog.coursegraph.com/?p=696

Coursera上Python课程(公开课)汇总推荐:从Python入门到应用Python

Deep Learning Specialization on Coursera

Python是深度学习时代的语言,Coursera上有很多Python课程,从Python入门到精通,从Python基础语法到应用Python,满足各个层次的需求,以下是Coursera上的Python课程整理,仅供参考,这里也会持续更新。

1. 密歇根大学的“Python for Everybody Specialization(人人都可以学习的Python专项课程)”

这个系列对于学习者的编程背景和数学要求几乎为零,非常适合Python入门学习。这个系列也是Coursera上最受欢迎的Python学习系列课程,强烈推荐。这个Python系列的目标是“通过Python学习编程并分析数据,开发用于采集,清洗,分析和可视化数据的程序(Learn to Program and Analyze Data with Python-Develop programs to gather, clean, analyze, and visualize data.” ,以下是关于这个系列的简介:

This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.

这个系列包含4门子课程和1门毕业项目课程,包括Python入门基础,Python数据结构, 使用Python获取网络数据(Python爬虫),在Python中使用数据库以及Python数据可视化等。以下是具体子课程的介绍:

1.1 Programming for Everybody (Getting Started with Python)

Python入门级课程,这门课程暂且翻译为“人人都可以学编程-从Python开始”,如果没有任何编程基础,就从这门课程开始吧:

This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.

1.2 Python Data Structures(Python数据结构)

Python基础课程,这门课程的目标是介绍Python语言的核心数据结构(This course will introduce the core data structures of the Python programming language.),关于这门课程:

This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”. This course covers Python 3.

1.3 Using Python to Access Web Data(使用Python获取网页数据--Python爬虫)

Python应用课程,只有使用Python才能学以致用,这门课程的目标是展示如何通过爬取和分析网页数据将互联网作为数据的源泉(This course will show how one can treat the Internet as a source of data):

This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.

1.4 Using Databases with Python(Python数据库)

Python应用课程,在Python中使用数据库。这门课程的目标是在Python中学习SQL,使用SQLite3作为抓取数据的存储数据库:

This course will introduce students to the basics of the Structured Query Language (SQL) as well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort. The course will use SQLite3 as its database. We will also build web crawlers and multi-step data gathering and visualization processes. We will use the D3.js library to do basic data visualization. This course will cover Chapters 14-15 of the book “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-13 of the textbook and the first three courses in this specialization. This course covers Python 3.

1.5 Capstone: Retrieving, Processing, and Visualizing Data with Python(毕业项目课程:使用Python获取,处理和可视化数据)

Python应用实践课程,这是这个系列的毕业项目课程,目的是通过开发一系列Python应用项目让学生熟悉Python抓取,处理和可视化数据的流程。

In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.

2. 多伦多大学的编程入门课程"Learn to Program: The Fundamentals(学习编程:基础) "

Python入门级课程。这门课程以Python语言传授编程入门知识,实为零基础的Python入门课程。感兴趣的同学可以参考课程图谱上的老课程评论 :http://coursegraph.com/coursera_programming1 ,之前一个同学的评价是 “两个老师语速都偏慢,讲解细致,又有可视化工具Python Visualizer用于详细了解程序具体执行步骤,可以说是零基础学习python编程的最佳选择。”

Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language.

3. 莱斯大学的Python专项课程系列:Introduction to Scripting in Python Specialization

入门级Python学习系列课程,涵盖Python基础, Python数据表示, Python数据分析, Python数据可视化等子课程,比较适合Python入门。这门课程的目标是让学生可以在处理实际问题是使用Python解决问题:Launch Your Career in Python Programming-Master the core concepts of scripting in Python to enable you to solve practical problems.

This specialization is intended for beginners who would like to master essential programming skills. Through four courses, you will cover key programming concepts in Python 3 which will prepare you to use Python to perform common scripting tasks. This knowledge will provide a solid foundation towards a career in data science, software engineering, or other disciplines involving programming.

这个系列包含4门子课程,以下是具体子课程的介绍:

3.1 Python Programming Essentials(Python编程基础)

Python入门基础课程,这门课程将讲授Python编程基础知识,包括表达式,变量,函数等,目标是让用户熟练使用Python:

This course will introduce you to the wonderful world of Python programming! We'll learn about the essential elements of programming and how to construct basic Python programs. We will cover expressions, variables, functions, logic, and conditionals, which are foundational concepts in computer programming. We will also teach you how to use Python modules, which enable you to benefit from the vast array of functionality that is already a part of the Python language. These concepts and skills will help you to begin to think like a computer programmer and to understand how to go about writing Python programs. By the end of the course, you will be able to write short Python programs that are able to accomplish real, practical tasks. This course is the foundation for building expertise in Python programming. As the first course in a specialization, it provides the necessary building blocks for you to succeed at learning to write more complex Python programs. This course uses Python 3. While many Python programs continue to use Python 2, Python 3 is the future of the Python programming language. This first course will use a Python 3 version of the CodeSkulptor development environment, which is specifically designed to help beginning programmers learn quickly. CodeSkulptor runs within any modern web browser and does not require you to install any software, allowing you to start writing and running small programs immediately. In the later courses in this specialization, we will help you to move to more sophisticated desktop development environments.

3.2 Python Data Representations(Python数据表示)

Python入门基础课程,这门课程依然关注Python的基础知识,包括Python字符串,列表等,以及Python文件操作:

This course will continue the introduction to Python programming that started with Python Programming Essentials. We'll learn about different data representations, including strings, lists, and tuples, that form the core of all Python programs. We will also teach you how to access files, which will allow you to store and retrieve data within your programs. These concepts and skills will help you to manipulate data and write more complex Python programs. By the end of the course, you will be able to write Python programs that can manipulate data stored in files. This will extend your Python programming expertise, enabling you to write a wide range of scripts using Python This course uses Python 3. While most Python programs continue to use Python 2, Python 3 is the future of the Python programming language. This course introduces basic desktop Python development environments, allowing you to run Python programs directly on your computer. This choice enables a smooth transition from online development environments.

3.3 Python Data Analysis(Python数据分析)

Python基础课程,这门课程将讲授通过Python读取和分析表格数据和结构化数据等,例如TCSV文件等:

This course will continue the introduction to Python programming that started with Python Programming Essentials and Python Data Representations. We'll learn about reading, storing, and processing tabular data, which are common tasks. We will also teach you about CSV files and Python's support for reading and writing them. CSV files are a generic, plain text file format that allows you to exchange tabular data between different programs. These concepts and skills will help you to further extend your Python programming knowledge and allow you to process more complex data. By the end of the course, you will be comfortable working with tabular data in Python. This will extend your Python programming expertise, enabling you to write a wider range of scripts using Python. This course uses Python 3. While most Python programs continue to use Python 2, Python 3 is the future of the Python programming language. This course uses basic desktop Python development environments, allowing you to run Python programs directly on your computer.

3.4 Python Data Visualization(Python数据可视化)

Python应用课程,这门课程将基于前3门课程学习的Python知识,抓取网络数据,然后清洗,处理和分析数据,并最终可视化呈现数据:

This if the final course in the specialization which builds upon the knowledge learned in Python Programming Essentials, Python Data Representations, and Python Data Analysis. We will learn how to install external packages for use within Python, acquire data from sources on the Web, and then we will clean, process, analyze, and visualize that data. This course will combine the skills learned throughout the specialization to enable you to write interesting, practical, and useful programs. By the end of the course, you will be comfortable installing Python packages, analyzing existing data, and generating visualizations of that data. This course will complete your education as a scripter, enabling you to locate, install, and use Python packages written by others. You will be able to effectively utilize tools and packages that are widely available to amplify your effectiveness and write useful programs.

4. 莱斯大学的计算(机)基础专项课程系列:Fundamentals of Computing Specialization

入门级Python编程学习课程系列,这个系列覆盖了大部分莱斯大学一年级计算机科学新生的学习材料,学生通过Python学习现代编程语言技巧,并将这些技巧应用到20个左右的有趣的编程项目中。

This Specialization covers much of the material that first-year Computer Science students take at Rice University. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.

这个系列包括Python交互式编程设计,计算原理,算法思维等6门课程和1门毕业项目课程,目标是让学生像计算机科学家一样编程和思考(Learn how to program and think like a Computer Scientist),以下是子课程的相关介绍:

4.1 An Introduction to Interactive Programming in Python (Part 1)(Python交互式编程导论上)

Python入门级课程,这门课程将讲授Python编程基础知识,例如普通表达式,条件表达式和函数,并用这些知识构建一个简单的交互式应用。

This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple. These applications will involve windows whose contents are graphical and respond to buttons, the keyboard and the mouse. In part 1 of this course, we will introduce the basic elements of programming (such as expressions, conditionals, and functions) and then use these elements to create simple interactive applications such as a digital stopwatch. Part 1 of this class will culminate in building a version of the classic arcade game "Pong".

4.2 An Introduction to Interactive Programming in Python (Part 2)(Python交互式编程导论下)

Python入门级课程,这门课程将继续讲授Python基础知识,例如列表,词典和循环,并将使用这些知识构建一个简单的游戏例如Blackjack:

This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple. These applications will involve windows whose contents are graphical and respond to buttons, the keyboard and the mouse. In part 2 of this course, we will introduce more elements of programming (such as list, dictionaries, and loops) and then use these elements to create games such as Blackjack. Part 1 of this class will culminate in building a version of the classic arcade game "Asteroids". Upon completing this course, you will be able to write small, but interesting Python programs. The next course in the specialization will begin to introduce a more principled approach to writing programs and solving computational problems that will allow you to write larger and more complex programs.

4.3 Principles of Computing (Part 1)(计算原理上)

编程基础课程,这门课程聚焦在了编程的基础上,包括编码标准和测试,数学基础包括概率和组合等。

This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.

4.4 Principles of Computing (Part 2)(计算原理下)

编程基础课程,这门课程聚焦在搜索、排序、递归等主题上:

This two-part course introduces the basic mathematical and programming principles that underlie much of Computer Science. Understanding these principles is crucial to the process of creating efficient and well-structured solutions for computational problems. To get hands-on experience working with these concepts, we will use the Python programming language. The main focus of the class will be weekly mini-projects that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 2 of this course, the programming portion of the class will focus on concepts such as recursion, assertions, and invariants. The mathematical portion of the class will focus on searching, sorting, and recursive data structures. Upon completing this course, you will have a solid foundation in the principles of computation and programming. This will prepare you for the next course in the specialization, which will begin to introduce a structured approach to developing and analyzing algorithms. Developing such algorithmic thinking skills will be critical to writing large scale software and solving real world computational problems.

4.5 Algorithmic Thinking (Part 1)(算法思维上)

编程基础课程,这门课程聚焦在算法思维的培养上,讲授图算法的相关概念并用Python实现:

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".

4.6 Algorithmic Thinking (Part 2)(算法思维下)

编程基础课程,这门课程聚焦在培养学生的算法思维,并了解一些高级算法主题,例如分治法,动态规划等:

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems. In part 2 of this course, we will study advanced algorithmic techniques such as divide-and-conquer and dynamic programming. As the central part of the course, students will implement several algorithms in Python that incorporate these techniques and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Once students have completed this class, they will have both the mathematical and programming skills to analyze, design, and program solutions to a wide range of computational problems. While this class will use Python as its vehicle of choice to practice Algorithmic Thinking, the concepts that you will learn in this class transcend any particular programming language.

4.7 The Fundamentals of Computing Capstone Exam(计算基础毕业项目课程)

Python应用课程,基于以上子课程的学习,计算基础毕业项目课程将用Python和所学的知识完成 20+ 项目:

While most specializations on Coursera conclude with a project-based course, students in the "Fundamentals of Computing" specialization have completed more than 20+ projects during the first six courses of the specialization. Given that much of the material in these courses is reused from session to session, our goal in this capstone class is to provide a conclusion to the specialization that allows each student an opportunity to demonstrate their individual mastery of the material in the specialization. With this objective in mind, the focus in this Capstone class will be an exam whose questions are updated periodically. This approach is designed to help insure that each student is solving the exam problems on his/her own without outside help. For students that have done their own work, we do not anticipate that the exam will be particularly hard. However, those students who have relied too heavily on outside help in previous classes may have a difficult time. We believe that this approach will increase the value of the Certificate for this specialization.

5. 密歇根大学的 Applied Data Science with Python(Python数据科学应用专项课程系列)

Python应用系列课程,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,例如pandas, matplotlib, scikit-learn, nltk以及networkx等Python工具。

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

这个系列课程有5门课程,包括Python数据科学导论课程(Introduction to Data Science in Python),Python数据可视化(Applied Plotting, Charting & Data Representation in Python),Python机器学习(Applied Machine Learning in Python) ,Python文本挖掘(Applied Text Mining in Python) , Python社交网络分析(Applied Social Network Analysis in Python),以下是具体子课程的介绍:

5.1 Introduction to Data Science in Python(Python数据科学导论)

Python基础和应用课程,这门课程从Python基础讲起,然后通过pandas数据科学库介绍DataFrame等数据分析中的核心数据结构概念,让学生学会操作和分析表格数据并学会运行基础的统计分析工具。

This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

5.2 Applied Plotting, Charting & Data Representation in Python(Python数据可视化)

Python应用课程,这门课程聚焦在通过使用matplotlib库进行数据图表的绘制和可视化呈现:

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will describe the gamut of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

5.3 Applied Machine Learning in Python(Python机器学习)

Python应用课程,这门课程主要聚焦在通过Python应用机器学习,包括机器学习和统计学的区别,机器学习工具包scikit-learn的介绍,有监督学习和无监督学习,数据泛化问题(例如交叉验证和过拟合)等。

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

5.4 Applied Text Mining in Python(Python文本挖掘)

Python应用课程,这门课程主要聚焦在文本挖掘和文本分析基础,包括正则表达式,文本清洗,文本预处理等,并结合NLTK讲授自然语言处理的相关知识,例如文本分类,主题模型等。

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

5.5 Applied Social Network Analysis in Python(Python社交网络分析)

Python应用课程,这门课程通过Python工具包 NetworkX 介绍社交网络分析的相关知识。

This course will introduce the learner to network analysis through the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness.. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

您可以继续在课程图谱上挖掘Coursera上新的Python课程,也欢迎推荐到这里。

注:本文首发“课程图谱博客”:http://blog.coursegraph.com ,同步发布到这里,原文链接地址:http://blog.coursegraph.com/coursera%E4%B8%8Apython%E8%AF%BE%E7%A8%8B%EF%BC%88%E5%85%AC%E5%BC%80%E8%AF%BE%EF%BC%89%E6%B1%87%E6%80%BB%E6%8E%A8%E8%8D%90%EF%BC%9A%E4%BB%8Epython%E5%85%A5%E9%97%A8%E5%88%B0%E5%BA%94%E7%94%A8python