其次你得有一定的数学基础，如果你觉得微积分、线性代数和概率统计这些基础课程没有时间回顾的话，那么推荐机器学习强相关的一门课和一本书：

1）面向机器学习的数学课程（Mathematics for Machine Learning Specialization）

http://coursegraph.com/coursera-specializations-mathematics-machine-learning

伦敦帝国理工学院的面向机器学习的数学专项课程系列（Mathematics for Machine Learning Specialization），该系列包含3门子课程，涵盖线性代数，多变量微积分，以及主成分分析（PCA），这个专项系列课程的目标是弥补数学与机器学习以及数据科学鸿沟，感兴趣的同学可以关注：Mathematics for Machine Learning。Learn about the prerequisite mathematics for applications in data science and machine learning

2）机器学习中的数学（https://mml-book.github.io/）：新书，看章节安排的很合理。

如果想学习更多的数学课程打基础，这里有：

除了数学基础，你还应该有一定的编程基础，如果你还是编程小白，那么推荐从Python入门：

当然，一定的数据结构和算法接触还是要有的：

5）Coursera上数据结构 & 算法课程（公开课）汇总推荐

然后重点推荐推荐2门机器学习入门课程：

6） 还是首推Andrew Ng老师的机器学习课程了：http://coursegraph.com/coursera_ml

7） 其次是最近比较火的fast.ai的课程：fast.ai 新课程：面向程序员的机器学习导论（Introduction to Machine Learning for Coders）

不过到了这里，也只是入门而已，你需要动手做相关的项目，如果没有项目，我推荐你去参加一下类似Kaggle的项目。如果想更深入的学习更多的机器学习课程，这里有一份Coursera课程汇总：

最后，如果想更进一步，学习深度学习，依然首推Andrew Ng的深度学习专项课程：

9）Deep Learning Specialization：http://coursegraph.com/coursera-specializations-deep-learning

再奉上一份更详细的深度学习课程资源，仅供参考：

10）深度学习课程资源整理

注：原创文章，转载请注明出处“课程图谱博客”：http://blog.coursegraph.com

本文链接地址：http://blog.coursegraph.com/如何学习机器学习-机器学习该怎么入门 http://blog.coursegraph.com/?p=904

]]>Welcome to Introduction to Machine Learning for Coders! taught by Jeremy Howard (Kaggle’s #1 competitor 2 years running, and founder of Enlitic). Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building data products.

课程授课者是 Jeremy Howard，Kaggle前冠军选手和专家，fast.ai的发起者之一，关于Jeremy Howard, 以下是来自雷锋网《Enlitic创始人Jeremy Howard专访：我眼中的深度学习与数据科学》中的介绍：

他是Enlitic、FastMail、Optimal Decisions Group三家科技公司的创始人兼CEO，是大数据竞赛平台Kaggle的前主席和首席科学家，是美国奇点大学（Singularity University）最年轻的教职工，是在2014达沃斯论坛上发表主题演讲的全球青年领袖，他在 TED 上的演讲《The wonderful and terrifying implications of computers that can learn》收获了近200万的点击…

这门机器学习课程包含12节课，比较偏重随机森林，第一节就从从随机森林讲起，后续几个章节相关，另外涉及性能评估、模型验证和融合、特征提取、梯度下降、逻辑回归以及NLP相关的内容:

Lesson 1 – Introduction to Random Forests

Lesson 2 – Random Forest Deep Dive

Lesson 3 – Performance, Validation and Model Interpretation

Lesson 4 – Feature Importance, Tree Interpreter

Lesson 5 – Extrapolation and RF from Scratch

Lesson 6 – Data Products

Lesson 7 – Introduction to Random Forests

Lesson 8 – Gradient Descent and Logistic Regression

Lesson 9 – Regularization, Learning Rates and NLP

Lesson 10 – More NLP, and Columnar Data

Lesson 11 – Embeddings

Lesson 12 – Complete Rossmann, Ethical Issues

关于这门面向程序员的机器学习课程，fast.ai官方有个详细介绍的文章，感兴趣的同学可以参考：http://www.fast.ai/2018/09/26/ml-launch/

最后欢迎关注我们的公众号：

注：原创文章，转载请注明出处“课程图谱博客”：http://blog.coursegraph.com

本文链接地址：http://blog.coursegraph.com/fast-ai-新课程面向程序员的机器学习导论-introduction-to-machine-learning-for-coders http://blog.coursegraph.com/?p=887

]]>这个专项课程的主要目标是为金融相关的机器学习核心范式和算法奠定坚实的基础而提供必要的知识和实战技能，特别关注机器学习在金融投资中不同的实际问题中的应用。

该系列旨在帮助学生解决他们在现实生活中可能遇到的实际的机器学习问题，包括：

（1）将问题映射到可用的机器学习方法的泛化场景，

（2）选择最适合解决问题的特定机器学习方法，以及

（3）成功实施解决方案，并评估其性能。

该专业课程面向三类学生设计：

· 在银行，资产管理公司或对冲基金等金融机构工作的从业人员

· 对将机器学习应用于日内交易感兴趣的个人

· 目前正在攻读金融学，统计学，计算机科学，数学，物理学，工程学或其他相关学科的学位的全日制学生，这些学生希望了解机器学习在金融领域的实际应用。

这个专项课程由纽约大学推出，包含4门子课程：

Guided Tour of Machine Learning in Finance（金融中的机器学习导览）

本课程的目的是提供一个关于机器学习领域的介绍和广泛的概括，重点是机器学习在金融中的应用。目标是让学生了解机器学习是什么，机器学习面向的是什么以及它可以应用于多少不同的金融问题。

This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.

Fundamentals of Machine Learning in Finance(金融中的机器学习基础知识)

该课程旨在帮助学生解决他们在现实生活中可能遇到的实际机器学习问题，包括：（1）理解所面临的问题并且能够找到合适的机器学习方法大致框架，（2）知道哪个特定的机器学习方法最适合解决该问题，（3）拥有成功实施解决方案并评估其性能的能力。具有一些或不具备机器学习知识的学习者将了解有监督学习和无监督学习，以及强化学习的主要算法，并且将能够使用机器学习开源Python包来设计，测试和实现金融中的机器学习算法。金融机器学习的基础知识将提供更深入的有监督，无监督和强化学习内容，课程将以一个使用无监督学习来实现简单投资组合交易策略的项目作为结束。

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

Reinforcement Learning in Finance（金融中的强化学习)

本课程旨在介绍强化学习的基本概念，并开发用于期权评估，交易和资产管理的强化学习应用的用例。先修课程是“金融中的机器学习导览”和“金融中的机器学习基础”课程。

This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management. Prerequisites are the courses “Guided Tour of Machine Learning in Finance” and “Fundamentals of Machine Learning in Finance”.

Overview of Advanced Methods of Reinforcement Learning in Finance（强化学习在金融中的高级方法概述）

在这个系列的最后一个课程“强化学习在金融中的高级方法概述”中，将深入研究第三门课程“金融中的强化学习”中讨论的主题。特别是将讨论强化学习，期权定价和物理学之间的联系，逆向强化学习对建模市场影响和价格动态的影响，以及强化学习中的感知行动周期。最后，将概述强化学习在高频交易，加密货币，点对点借贷等方面的趋势和潜在应用。

In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance. In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high frequency trading, cryptocurrencies, peer-to-peer lending, and more.

注：原创文章，转载请注明出处“课程图谱博客”：http://blog.coursegraph.com

本文链接地址：http://blog.coursegraph.com/coursera课程金融中的机器学习和强化学习-machine-learning-and-reinforcement-learning-in-finance http://blog.coursegraph.com/?p=867

]]>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

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

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：原创文章，转载请注明出处“课程图谱博客”：http://blog.coursegraph.com

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

]]>1 斯坦福大学 Introduction to Mathematical Thinking（数学思维导论）

http://coursegraph.com/coursera-mathematical-thinking

引用老版课程一个同学的评价，供参考：

这门课是高中数学到大学数学的一个过度。高中数学一般重计算不太注重证明，这门课讲了基本的逻辑，数学语言（两个 quantifier，there exists， for all）和证明的几个基本方法，比如证明充要条件要从两个方向证、证伪只需要举个反例，原命题不好证的时候可以证等价的逆否命题以及很常用的数学归纳法。课程讲了数论里一些基本定理，然后通过让你证一些看起来显然而不需要证明的证明题来训练你证明的技能和逻辑思考的能力，看起来显然的命题也是要证明才能说服人的，课程最后简略的讲了下数学分析里面实数的引入，但这部分讲的不完整。Keith Devlin 是个 old school 的讲师，上课只用纸和笔，也是属于比较热情的讲师，他每周都会录几个答疑的视频。这门比较适合大一的新生上，开得也比较频繁。

课程简介：

Learn how to think the way mathematicians do – a powerful cognitive process developed over thousands of years. Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box – a valuable ability in today’s world. This course helps to develop that crucial way of thinking.

2 加州大学尔湾分校 初级微积分系列课程

1）Pre-Calculus: Functions（初级微积分：函数）

http://coursegraph.com/coursera-pre-calculus

This course covers mathematical topics in college algebra, with an emphasis on functions. The course is designed to help prepare students to enroll for a first semester course in single variable calculus. Upon completing this course, you will be able to: 1. Solve linear and quadratic equations 2. Solve some classes of rational and radical equations 3. Graph polynomial, rational, piece-wise, exponential and logarithmic functions 4. Find integer roots of polynomial equations 5. Solve exponential and logarithm equations 6. Understand the inverse relations between exponential and logarithm equations 7. Compute values of exponential and logarithm expressions using basic properties

2）Pre-Calculus: Trigonometry(初级微积分：三角）

http://coursegraph.com/coursera-trigonometry

This course covers mathematical topics in trigonometry. Trigonometry is the study of triangle angles and lengths, but trigonometric functions have far reaching applications beyond simple studies of triangles. This course is designed to help prepare students to enroll for a first semester course in single variable calculus. Upon completing this course, you will be able to: 1. Evaluate trigonometric functions using the unit circle and right triangle approaches 2. Solve trigonometric equations 3. Verify trigonometric identities 4. Prove and use basic trigonometric identities. 5. Manipulate trigonometric expressions using standard identities 6. Solve right triangles 7. Apply the Law of Sines and the Law of Cosines

3 宾夕法尼亚大学的 单变量微积分系列课程

1）Calculus: Single Variable Part 1 – Functions（单变量微积分1：函数）

http://coursegraph.com/coursera-single-variable-calculus

Calculus is one of the grandest achievements of human thought, explaining everything from planetary orbits to the optimal size of a city to the periodicity of a heartbeat. This brisk course covers the core ideas of single-variable Calculus with emphases on conceptual understanding and applications. The course is ideal for students beginning in the engineering, physical, and social sciences. Distinguishing features of the course include: 1) the introduction and use of Taylor series and approximations from the beginning; 2) a novel synthesis of discrete and continuous forms of Calculus; 3) an emphasis on the conceptual over the computational; and 4) a clear, dynamic, unified approach. In this first part–part one of five–you will extend your understanding of Taylor series, review limits, learn the *why* behind l’Hopital’s rule, and, most importantly, learn a new language for describing growth and decay of functions: the BIG O.

2）Calculus: Single Variable Part 2 – Differentiation（单变量微积分2：微分）

http://coursegraph.com/coursera-differentiation-calculus

Calculus is one of the grandest achievements of human thought, explaining everything from planetary orbits to the optimal size of a city to the periodicity of a heartbeat. This brisk course covers the core ideas of single-variable Calculus with emphases on conceptual understanding and applications. The course is ideal for students beginning in the engineering, physical, and social sciences. Distinguishing features of the course include: 1) the introduction and use of Taylor series and approximations from the beginning; 2) a novel synthesis of discrete and continuous forms of Calculus; 3) an emphasis on the conceptual over the computational; and 4) a clear, dynamic, unified approach. In this second part–part two of five–we cover derivatives, differentiation rules, linearization, higher derivatives, optimization, differentials, and differentiation operators.

3）Calculus: Single Variable Part 3 – Integration（单变量微积分3：积分）

http://coursegraph.com/coursera-integration-calculus

Calculus is one of the grandest achievements of human thought, explaining everything from planetary orbits to the optimal size of a city to the periodicity of a heartbeat. This brisk course covers the core ideas of single-variable Calculus with emphases on conceptual understanding and applications. The course is ideal for students beginning in the engineering, physical, and social sciences. Distinguishing features of the course include: 1) the introduction and use of Taylor series and approximations from the beginning; 2) a novel synthesis of discrete and continuous forms of Calculus; 3) an emphasis on the conceptual over the computational; and 4) a clear, dynamic, unified approach. In this third part–part three of five–we cover integrating differential equations, techniques of integration, the fundamental theorem of integral calculus, and difficult integrals.

4) Calculus: Single Variable Part 4 – Applications（单变量微积分4：应用）

http://coursegraph.com/coursera-applications-calculus

Calculus is one of the grandest achievements of human thought, explaining everything from planetary orbits to the optimal size of a city to the periodicity of a heartbeat. This brisk course covers the core ideas of single-variable Calculus with emphases on conceptual understanding and applications. The course is ideal for students beginning in the engineering, physical, and social sciences. Distinguishing features of the course include: 1) the introduction and use of Taylor series and approximations from the beginning; 2) a novel synthesis of discrete and continuous forms of Calculus; 3) an emphasis on the conceptual over the computational; and 4) a clear, dynamic, unified approach. In this fourth part–part four of five–we cover computing areas and volumes, other geometric applications, physical applications, and averages and mass. We also introduce probability.

4 杜克大学 Data Science Math Skills（数据科学中的数学技巧）

http://coursegraph.com/coursera-datasciencemathskills

这门课程主要介绍数据科学中涉及的相关数学概念，让学生了解基本的数学概念，掌握基本的数学语言，内容涵盖集合论、求和的Sigma符号、数学上的笛卡尔（x，y）平面、指数、对数和自然对数函数，概率论以及叶斯定理等：

Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material.

5 加州大学圣迭戈分校 Introduction to Discrete Mathematics for Computer Science Specialization（面向计算机科学的离散数学专项课程)

http://coursegraph.com/coursera-specializations-discrete-mathematics

面向计算机科学的离散数学专项课程（Introduction to Discrete Mathematics for Computer Science Specialization），这个系列包含5门子课程，涵盖证明、组合数学与概率、图论，数论和密码学，配送问题项目等，感兴趣的同学可以关注: Build a Foundation for Your Career in IT-Master the math powering our lives and prepare for your software engineer or security analyst career

Discrete Math is needed to see mathematical structures in the object you work with, and understand their properties. This ability is important for software engineers, data scientists, security and financial analysts (it is not a coincidence that math puzzles are often used for interviews). We cover the basic notions and results (combinatorics, graphs, probability, number theory) that are universally needed. To deliver techniques and ideas in discrete mathematics to the learner we extensively use interactive puzzles specially created for this specialization. To bring the learners experience closer to IT-applications we incorporate programming examples, problems and projects in our courses.

http://coursegraph.com/coursera-what-is-a-proof

There is a perceived barrier to mathematics: proofs. In this course we will try to convince you that this barrier is more frightening than prohibitive: most proofs are easy to understand if explained correctly, and often they are even fun. We provide an accompanied excursion in the “proof zoo” showing you examples of techniques of different kind applied to different topics. We use some puzzles as examples, not because they are “practical”, but because discussing them we learn important reasoning and problem solving techniques that are useful. We hope you enjoy playing with the puzzles and inventing/understandings the proofs. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.

2）Combinatorics and Probability（组合和概率）

Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main `consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.

3）Introduction to Graph Theory（图论导论）

We invite you to a fascinating journey into Graph Theory — an area which connects the elegance of painting and the rigor of mathematics; is simple, but not unsophisticated. Graph Theory gives us, both an easy way to pictorially represent many major mathematical results, and insights into the deep theories behind them. In this course, among other intriguing applications, we will see how GPS systems find shortest routes, how engineers design integrated circuits, how biologists assemble genomes, why a political map can always be colored using a few colors. We will study Ramsey Theory which proves that in a large system, complete disorder is impossible! By the end of the course, we will implement an algorithm which finds an optimal assignment of students to schools. This algorithm, developed by David Gale and Lloyd S. Shapley, was later recognized by the conferral of Nobel Prize in Economics. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.

4) Number Theory and Cryptography（数论和密码学）

We all learn numbers from the childhood. Some of us like to count, others hate it, but any person uses numbers everyday to buy things, pay for services, estimated time and necessary resources. People have been wondering about numbers’ properties for thousands of years. And for thousands of years it was more or less just a game that was only interesting for pure mathematicians. Famous 20th century mathematician G.H. Hardy once said “The Theory of Numbers has always been regarded as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the word nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this short course, we will make the whole journey from the foundation to RSA in 4 weeks. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.

5）Solving Delivery Problem（解决旅行商问题）

http://coursegraph.com/coursera-delivery-problem

We’ll implement together an efficient program for a problem needed by delivery companies all over the world millions times per day — the travelling salesman problem. The goal in this problem is to visit all the given places as quickly as possible. How to find an optimal solution to this problem quickly? We still don’t have provably efficient algorithms for this difficult computational problem and this is the essence of the P versus NP problem, the most important open question in Computer Science. Still, we’ll implement several efficient solutions for real world instances of the travelling salesman problem. While designing these solutions, we will rely heavily on the material learned in the courses of the specialization: proof techniques, combinatorics, probability, graph theory. We’ll see several examples of using discrete mathematics ideas to get more and more efficient solutions.

6 伦敦帝国理工学院 Mathematics for Machine Learning Specialization（面向机器学习的数学专项课程系列）

http://coursegraph.com/coursera-specializations-mathematics-machine-learning

伦敦帝国理工学院的面向机器学习的数学专项课程系列（Mathematics for Machine Learning Specialization），该系列包含3门子课程，涵盖线性代数，多变量微积分，以及主成分分析（PCA），这个专项系列课程的目标是弥补数学与机器学习以及数据科学鸿沟，感兴趣的同学可以关注：Mathematics for Machine Learning。Learn about the prerequisite mathematics for applications in data science and machine learning

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in maths – stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialisation aims to bridge that gap, getting you up to speed in the underlying maths, building an intuitive understanding, and relating it to Machine Learning and Data Science. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimise fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third course, Dimensionality Reduction with Principal Components Analysis, uses the maths from the first two courses to do simple optimisation for the situation where you don’t have an understanding of how the data variables relate to each other. At the end of this specialisation you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

1） Mathematics for Machine Learning: Linear Algebra（面向机器学习的数学：线性代数）

http://coursegraph.com/coursera-linear-algebra-machine-learning

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets – like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we’re aiming at data-driven applications, we’ll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you’ll write code blocks and encounter Jupyter notebooks in Python, but don’t worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

2）Mathematics for Machine Learning: Multivariate Calculus（面向机器学习的数学：多变量微积分）

http://coursegraph.com/coursera-multivariate-calculus-machine-learning

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.

3）Mathematics for Machine Learning: PCA（面向机器学习的数学：主成分分析）

http://coursegraph.com/coursera-pca-machine-learning

This course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We’ll cover some basic statistics of data sets, such as mean values and variances, we’ll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we’ll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you’ll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you’ll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge.

注：原创文章，转载请注明出处“课程图谱博客”：http://blog.coursegraph.com

本文链接地址：http://blog.coursegraph.com/coursera上数学类相关课程数学公开课汇总推荐 http://blog.coursegraph.com/?p=804

]]>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：原创文章，转载请注明出处“课程图谱博客”：http://blog.coursegraph.com

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

]]>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 1 和 Algorithms: 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：原创文章，转载请注明出处“课程图谱博客”：http://blog.coursegraph.com

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]]>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 和 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：原创文章，转载请注明出处“课程图谱博客”：http://blog.coursegraph.com

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

]]>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：原创文章，转载请注明出处“课程图谱博客”：http://blog.coursegraph.com

]]>1. Andrew Ng (吴恩达) 深度学习专项课程 by Coursera and deeplearning.ai

这是 Andrew Ng 老师离开百度后推出的第一个深度学习项目（deeplearning.ai)的一个课程: Deep Learning Specialization ，课程口号是：Master Deep Learning, and Break into AI. 作为 Coursera 联合创始人 和 机器学习网红课程 “Machine Learning” 的授课者，Andrew Ng 老师引领了数百万同学进入了机器学习领域，而这门深度学习课程的口号也透露了他的野心：继续带领百万人进入深度学习的圣地。

作为 Andrew Ng 老师的粉丝，依然推荐这门课程作为深度学习入门课程首选，并且建议花费上 Coursera 上的课程，一方面可以做题，另外还有证书，最重要的是它的编程作业，是理解课程内容的关键点，仅仅看视频绝对是达不到这个效果的。参考：《Andrew Ng 深度学习课程小记》和《Andrew Ng (吴恩达) 深度学习课程小结》。

2. Geoffrey Hinton 大神的 面向机器学习的神经网络（Neural Networks for Machine Learning）

Geoffrey Hinton大神的这门深度学习课程 2012年在 Coursera 上开过一轮，之后一直沉寂，直到 Coursera 新课程平台上线，这门课程已开过多轮次，来自课程图谱网友的评论：

“Deep learning必修课”

“宗派大师+开拓者直接讲课，秒杀一切二流子”

这门深度学习课程相对上面 Andrew Ng深度学习课程有一定难道，但是没有编程作业，只有Quiz.

3. 牛津大学深度学习课程(2015): Deep learning at Oxford 2015

这门深度学习课程名字虽然是 “Machine Learning 2014-2015″，不过主要聚焦在深度学习的内容上，可以作为一门很系统的机器学习深度学习课程：

Machine learning techniques enable us to automatically extract features from data so as to solve predictive tasks, such as speech recognition, object recognition, machine translation, question-answering, anomaly detection, medical diagnosis and prognosis, automatic algorithm configuration, personalisation, robot control, time series forecasting, and much more. Learning systems adapt so that they can solve new tasks, related to previously encountered tasks, more efficiently.

The course focuses on the exciting field of deep learning. By drawing inspiration from neuroscience and statistics, it introduces the basic background on neural networks, back propagation, Boltzmann machines, autoencoders, convolutional neural networks and recurrent neural networks. It illustrates how deep learning is impacting our understanding of intelligence and contributing to the practical design of intelligent machines.

视频Playlist：https://www.youtube.com/playlist?list=PLE6Wd9FR–EfW8dtjAuPoTuPcqmOV53Fu

参考：“牛津大学Nando de Freitas主讲的机器学习课程，重点介绍深度学习，还请来Deepmind的Alex Graves和Karol Gregor客座报告，内容、讲解都属一流，强烈推荐！ 云: http://t.cn/RA2vSNX”

4. Udacity 深度学习（中/英）by Google

Udacity (优达学城）上由Google工程师主讲的免费深度学习课程，结合Google自己的深度学习工具 Tensorflow ，很不错：

机器学习是发展最快、最令人兴奋的领域之一，而深度学习则代表了机器学习中最前沿但也最有风险的一部分。在本课内容中，你将透彻理解深度学习的动机，并设计用于了解复杂和/或大量数据库的智能系统。

我们将教授你如何训练和优化基本神经网络、卷积神经网络和长短期记忆网络。你将通过项目和任务接触完整的机器学习系统 TensorFlow。你将学习解决一系列曾经以为非常具有挑战性的新问题，并在你用深度学习方法轻松解决这些问题的过程中更好地了解人工智能的复杂属性。

我们与 Google 的首席科学家兼 Google 智囊团技术经理 Vincent Vanhoucke 联合开发了本课内容。此课程提供中文版本。

5. Udacity 纳米基石学位项目：深度学习

Udacity的纳米基石学位项目，收费课程，不过据说更注重实战：

人工智能正颠覆式地改变着我们的世界，而背后推动这场进步的，正是深度学习技术。优达学城和硅谷技术明星一起，带来这门帮你系统性入门的课程。你将通过充满活力的硅谷课程内容、独家实战项目和专业代码审阅，快速掌握深度学习的基础知识和前沿应用。

你在实战项目中的每行代码都会获得专业审阅和反馈，还可以在同步学习小组中，接受学长、导师全程的辅导和督促

6. fast.ai 上的深度学习系列课程

fast.ai上提供了几门深度学习课程，课程标语很有意思：Making neural nets uncool again ，并且 Our courses (all are free and have no ads):

Deep Learning Part 1: Practical Deep Learning for Coders

Why we created the course

What we cover in the course

Deep Learning Part 2: Cutting Edge Deep Learning for Coders

Computational Linear Algebra: Online textbook and Videos

Providing a Good Education in Deep Learning—our teaching philosophy

A Unique Path to Deep Learning Expertise—our teaching approach

7. 台大李宏毅老师深度学习课程：Machine Learning and having it Deep and Structured

难得的免费中文深度学习课程：

课程主页：http://speech.ee.ntu.edu.tw/~tlkagk/courses_MLDS17.html

课程视频Playlist: https://www.youtube.com/playlist?list=PLJV_el3uVTsPMxPbjeX7PicgWbY7F8wW9

B站搬运深度学习课程视频: https://www.bilibili.com/video/av9770302/

8. 台大陈缊侬老师深度学习应用课程：Applied Deep Learning / Machine Learning and Having It Deep and Structured

据说是美女老师，这门课程16年秋季开过一次，不过没有视频，最新的这期是17年秋季课程，刚刚开课，Youtube上正在陆续放出课程视频：

16年课程主页，有Slides等相关资料：https://www.csie.ntu.edu.tw/~yvchen/f105-adl/index.html

17年课程主页，资料正在陆续放出：https://www.csie.ntu.edu.tw/~yvchen/f106-adl/

Youtube视频，目前没有playlist，可以关注其官方号放出的视频：https://www.youtube.com/channel/UCyB2RBqKbxDPGCs1PokeUiA/videos

9. Yann Lecun 深度学习公开课

“Yann Lecun 在 2016 年初于法兰西学院开课，这是其中关于深度学习的 8 堂课。当时是用法语授课，后来加入了英文字幕。

作为人工智能领域大牛和 Facebook AI 实验室（FAIR）的负责人，Yann Lecun 身处业内机器学习研究的最前沿。他曾经公开表示，现有的一些机器学习公开课内容已经有些过时。通过 Yann Lecun 的课程能了解到近几年深度学习研究的最新进展。该系列可作为探索深度学习的进阶课程。”

10. 2016 年蒙特利尔深度学习暑期班

推荐理由：看看嘉宾阵容吧，Yoshua Bengio 教授循环神经网络，Surya Ganguli 教授理论神经科学与深度学习理论，Sumit Chopra 教授 reasoning summit 和 attention，Jeff Dean 讲解 TensorFlow 大规模机器学习，Ruslan Salakhutdinov 讲解学习深度生成式模型，Ryan Olson 讲解深度学习的 GPU 编程，等等。

11. 斯坦福大学深度学习应用课程：CS231n: Convolutional Neural Networks for Visual Recognition

这门面向计算机视觉的深度学习课程由Fei-Fei Li教授掌舵，内容面向斯坦福大学学生，货真价实，评价颇高：

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge.

12. 斯坦福大学深度学习应用课程: Natural Language Processing with Deep Learning

这门课程由NLP领域的大牛 Chris Manning 和 Richard Socher 执掌，绝对是学习深度学习自然语言处理的不二法门。

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models behind NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this winter quarter course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a thorough introduction to cutting-edge research in deep learning applied to NLP. On the model side we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some recent models involving a memory component. Through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems.

这门课程融合了两位授课者之前在斯坦福大学的授课课程，分别是自然语言处理课程 cs224n (Natural Language Processing)和面向自然语言处理的深度学习课程 cs224d (Deep Learning for Natural Language Processing).

13. 斯坦福大学深度学习课程: CS 20SI: Tensorflow for Deep Learning Research

准确的说，这门课程主要是针对深度学习工具Tensorflow的:

Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C++ API. For this course, we will be using Python.

This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.

14. 牛津大学 & DeepMind 联合的面向NLP的深度学习应用课程: Deep Learning for Natural Language Processing: 2016-2017

课程主页：https://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/

github课程项目页面：https://github.com/oxford-cs-deepnlp-2017/

课程视频Playlist: https://www.youtube.com/playlist?list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm

B站搬运视频: https://www.bilibili.com/video/av9817911/

15. 卡耐基梅隆大学（CMU）深度学习应用课程：CMU CS 11-747, Fall 2017 Neural Networks for NLP

课程主页：http://phontron.com/class/nn4nlp2017/

课程视频Playlist: https://www.youtube.com/watch?v=Sss2EA4hhBQ&list=PL8PYTP1V4I8ABXzdqtOpB_eqBlVAz_xPT

16. MIT组织的一个为期一周的深度学习课程: 6.S191: Introduction to Deep Learning http://introtodeeplearning.com/

17. 奈良先端科学技術大学院大学(NAIST) 2014年推出的一个深度学习短期课程（英文授课）：Deep Learning and Neural Networks

18. Deep Learning course: lecture slides and lab notebooks

欢迎大家推荐其他没有覆盖到的深度学习课程。

注：原创文章，转载请注明出处“课程图谱博客”：http://blog.coursegraph.com

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