标签归档:文本挖掘

Deep Learning Specialization on Coursera

Coursera专项课程推荐:谷歌云平台上基于TensorFlow的高级机器学习专项课程(Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization)

Coursera近期刚刚推了一门新专项课程:谷歌云平台上基于TensorFlow的高级机器学习专项课程(Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization),看起来很不错。这个系列包含5门子课程,涵盖端到端机器学习、生产环境机器学习系统、图像理解、面向时间序列和自然语言处理的序列模型、推荐系统等内容,感兴趣的同学可以关注:Learn Advanced Machine Learning with Google Cloud. Build production-ready machine learning models with TensorFlow on Google Cloud Platform.

课程链接:http://coursegraph.com/coursera-specializations-advanced-machine-learning-tensorflow-gcp

五门子课程分别是:

1、基于TensorFlow的端到端机器学习(End-to-End Machine Learning with TensorFlow on GCP)

http://coursegraph.com/coursera-end-to-end-ml-tensorflow-gcp

这门课程将首先回顾一下谷歌云平台上的TensorFlow机器学习专项课程系列(Machine Learning with TensorFlow on Google Cloud Platform Specialization)的相关内容。 回顾这些内容的最佳方法之一是就是使用学到的概念和技术。 因此,这门课程被设置为研讨会,在这个研讨会中,学员将在谷歌云平台上使用TensorFlow进行端到端机器学习。先决条件:基础的SQL知识,熟悉Python和TensorFlow。

In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization. One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform Prerequisites: Basic SQL, familiarity with Python and TensorFlow

2、生产环境机器学习系统(Production ML Systems)

http://coursegraph.com/coursera-gcp-production-ml-systems

在该系列的第二个课程中,将深入研究生产环境中高性能机器学习系统的组件和最佳实践。 先决条件:基础的SQL知识,熟悉Python和TensorFlow。

In the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments. Prerequisites: Basic SQL, familiarity with Python and TensorFlow

3、Image Understanding with TensorFlow on GCP(通过TensorFlow进行图像理解)

http://coursegraph.com/coursera-specializations-advanced-machine-learning-tensorflow-gcp

这是谷歌云平台高级机器学习专项课程系列第三课:通过TensorFlow进行图像理解。在这门课程中,首先将介绍使用卷积神经网络构建图像分类器的不同策略。其次将通过数据增强,特征提取和超参数调优来提高模型的准确性,同时避免过度拟合数据。学习过程中还将研究实际出现的问题,例如,当图像数据不足时如何处理问题以及如何将最新的研究成果纳入我们的模型。最后在这门课程的实践平台上,学员将在不同的公共数据集上构建和优化自己的图像分类器模型。先决条件:基础的SQL知识,熟悉Python和TensorFlow。

This is the third course of the Advanced Machine Learning on GCP specialization. In this course, We will take a look at different strategies for building an image classifier using convolutional neural networks. We’ll improve the model’s accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don’t have enough data and how to incorporate the latest research findings into our models. You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow

4、Sequence Models for Time Series and Natural Language Processing(面向时间序列和自然语言处理的序列模型)

http://coursegraph.com/coursera-sequence-models-tensorflow-gcp

谷歌云平台高级机器学习专项课程系列第四课:面向时间序列和自然语言处理的序列模型。这门课程将主要介绍序列模型及其应用,包括序列模型结构概览以及如何处理可变长输入。预测时间序列的未来值 • 对自由格式文本进行分类 • 使用递归神经网络解决时间序列和文本问题 • 在RNN/LSTM和更简单的模型之间进行选择 • 在文本问题中训练和重用词嵌入模型。在这门课程的实践平台上,学员将在不同的公共数据集上亲自构建和优化自己的文本分类器和序列模型。先决条件:基础的SQL知识,熟悉Python和TensorFlow。

This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. • Predict future values of a time-series • Classify free form text • Address time-series and text problems with recurrent neural networks • Choose between RNNs/LSTMs and simpler models • Train and reuse word embeddings in text problems You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow

5、Recommendation Systems with TensorFlow on GCP(基于TensorFlow的推荐系统)

http://coursegraph.com/coursera-recommendation-models-gcp

谷歌云平台高级机器学习专项课程系列第四课:基于TensorFlow的推荐系统。在本课程中,将应用分类模型和嵌入(embeddings)的知识来构建充当推荐引擎的机器学习组件。 设计基于内容的推荐引擎 • 实现一个协同过滤推荐引擎 • 构建具有用户和内容嵌入的混合推荐引擎。

In this course, you’ll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. • Devise a content-based recommendation engine • Implement a collaborative filtering recommendation engine • Build a hybrid recommendation engine with user and content embeddings

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:Coursera专项课程推荐:谷歌云平台上基于TensorFlow的高级机器学习专项课程(Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization) http://blog.coursegraph.com/?p=924

Coursera上数据科学相关课程(公开课)汇总推荐

Coursera上的数据科学课程有很多,这里汇总一批。

1、 Introduction to Data Science Specialization

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

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

2、Applied Data Science Specialization

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

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

3、Applied Data Science with Python Specialization

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

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

4、Data Science Specialization

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

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

5、Data Science at Scale Specialization

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

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

6、Advanced Data Science with IBM Specialization

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

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

7、Data Mining Specialization

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

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

8、Data Analysis and Interpretation Specialization

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

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

9、Executive Data Science Specialization

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

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

10、其他相关的数据科学课程

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

注: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

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

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

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

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

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

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

1.1 Programming for Everybody (Getting Started with Python)

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

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

1.2 Python Data Structures(Python数据结构)

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

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

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

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

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

1.4 Using Databases with Python(Python数据库)

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

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

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

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

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

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

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

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

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

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

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

这个系列包含4门子课程,以下是具体子课程的介绍:

3.1 Python Programming Essentials(Python编程基础)

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

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

3.2 Python Data Representations(Python数据表示)

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

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

3.3 Python Data Analysis(Python数据分析)

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

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

3.4 Python Data Visualization(Python数据可视化)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

编程基础课程,这门课程聚焦在搜索、排序、递归等主题上:

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

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

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

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

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

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

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

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

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

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

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

Python应用系列课程,这个系列的目标主要是通过Python编程语言介绍数据科学的相关领域,包括应用统计学,机器学习,信息可视化,文本分析和社交网络分析等知识,并结合一些流行的Python工具包,例如pandas, matplotlib, scikit-learn, nltk以及networkx等Python工具。

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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