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机器学习关键的几门课程_互联网上每门机器学习课程，均按您的评论排名

作者：Cpp五条 | 2024-04-13 03:51:41

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机器学习关键的几门课程

by David Venturi

大卫·文图里(David Venturi)

互联网上每门机器学习课程，均按您的评论排名 (Every single Machine Learning course on the internet, ranked by your reviews)

A year and a half ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead. And I could learn it faster, more efficiently, and for a fraction of the cost.

一年半以前，我退出了加拿大最好的计算机科学程序之一。我开始使用在线资源创建自己的数据科学硕士课程。我意识到我可以通过edX，Coursera和Udacity学习所需的一切。而且我可以更快，更有效地学习它，而费用却只有一小部分。

I’m almost finished now. I’ve taken many data science-related courses and audited portions of many more. I know the options out there, and what skills are needed for learners preparing for a data analyst or data scientist role. So I started creating a review-driven guide that recommends the best courses for each subject within data science.

我现在快要完蛋了。我参加了许多与数据科学相关的课程，并对更多课程进行了审计。我知道那里的选择，以及学习者准备数据分析师或数据科学家角色需要哪些技能。因此，我开始创建以评论为导向的指南，为数据科学中的每个学科推荐最佳课程。

For the first guide in the series, I recommended a few coding classes for the beginner data scientist. Then it was statistics and probability classes. Then introductions to data science. Also, data visualization.

对于本系列的第一个指南，我为初学者数据科学家推荐了一些编码类。然后是统计和概率分类。然后介绍数据科学。还有，数据可视化。

现在进入机器学习。 (Now onto machine learning.)

For this guide, I spent a dozen hours trying to identify every online machine learning course offered as of May 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings. My end goal was to identify the three best courses available and present them to you, below.

对于本指南，我花了十几个小时来尝试确定截至2017年5月提供的每个在线机器学习课程，从其教学大纲和评论中提取关键信息，并编制其评分。 我的最终目标是确定可用的三门最佳课程，并在下面向您介绍。

For this task, I turned to none other than the open source Class Central community, and its database of thousands of course ratings and reviews.

对于此任务，我只选择了开放源代码的Class Central社区及其包含数千个课程评分和评论的数据库。

Since 2011, Class Central founder Dhawal Shah has kept a closer eye on online courses than arguably anyone else in the world. Dhawal personally helped me assemble this list of resources.

自2011年以来， Class Central的创始人Dhawal Shah一直在关注在线课程，这一点可以说是世界上其他任何人所不及的。达瓦尔亲自帮助我整理了这份资源清单。

我们如何选择要考虑的课程 (How we picked courses to consider)

Each course must fit three criteria:

每门课程必须符合三个条件：

It must have a significant amount of machine learning content. Ideally, machine learning is the primary topic. Note that deep learning-only courses are excluded. More on that later.
它必须具有大量的机器学习内容。 理想情况下，机器学习是主要主题。请注意，仅深度学习课程被排除在外。以后再说。
It must be on-demand or offered every few months.
必须按需或每几个月提供一次。
It must be an interactive online course, so no books or read-only tutorials. Though these are viable ways to learn, this guide focuses on courses. Courses that are strictly videos (i.e. with no quizzes, assignments, etc.) are also excluded.
它必须是交互式的在线课程，因此没有书籍或只读教程 。尽管这些是可行的学习方法，但本指南重点介绍课程。严格讲授视频的课程(即没有测验，作业等)也被排除在外。

We believe we covered every notable course that fits the above criteria. Since there are seemingly hundreds of courses on Udemy, we chose to consider the most-reviewed and highest-rated ones only.

我们相信，我们涵盖了符合上述条件的所有重要课程。由于关于Udemy的课程似乎有数百种，因此我们选择只考虑评论次数最多和评分最高的课程。

There’s always a chance that we missed something, though. So please let us know in the comments section if we left a good course out.

不过，总有可能我们错过了一些东西。因此，如果我们留下了好的课程，请在评论部分让我们知道。

我们如何评估课程 (How we evaluated courses)

We compiled average ratings and number of reviews from Class Central and other review sites to calculate a weighted average rating for each course. We read text reviews and used this feedback to supplement the numerical ratings.

我们汇总了Class Central和其他评论网站的平均评分和评论数量，以计算每个课程的加权平均评分。我们阅读了文字评论，并使用此反馈来补充数字等级。

We made subjective syllabus judgment calls based on three factors:

我们基于以下三个因素进行了主观的课程提纲判断：

Explanation of the machine learning workflow. Does the course outline the steps required for executing a successful ML project? See the next section for what a typical workflow entails.
机器学习工作流程的说明。 该课程是否概述了执行成功的ML项目所需的步骤？有关典型工作流程的内容，请参见下一部分。
Coverage of machine learning techniques and algorithms. Are a variety of techniques (e.g. regression, classification, clustering, etc.) and algorithms (e.g. within classification: naive Bayes, decision trees, support vector machines, etc.) covered or just a select few? Preference is given to courses that cover more without skimping on detail.
涵盖机器学习技术和算法。 是否涵盖了各种技术(例如回归，分类，聚类等)和算法(例如，在分类内：朴素贝叶斯，决策树，支持向量机等)或仅选择了其中几种？优先选择涵盖更多内容而又不漏掉细节的课程。
Usage of common data science and machine learning tools. Is the course taught using popular programming languages like Python, R, and/or Scala? How about popular libraries within those languages? These aren’t necessary, but helpful so slight preference is given to these courses.
通用数据科学和机器学习工具的使用。 该课程是否使用流行的编程语言(例如Python，R和/或Scala)教授？这些语言中的流行图书馆怎么样？这些不是必需的，但有帮助，因此对这些课程略有偏爱。

什么是机器学习？什么是工作流程？ (What is machine learning? What is a workflow?)

A popular definition originates from Arthur Samuel in 1959: machine learning is a subfield of computer science that gives “computers the ability to learn without being explicitly programmed.” In practice, this means developing computer programs that can make predictions based on data. Just as humans can learn from experience, so can computers, where data = experience.

一个流行的定义源自1959年的亚瑟·塞缪尔 ( Arthur Samuel )：机器学习是计算机科学的一个子领域，它赋予“计算机无需明确编程即可学习的能力”。 在实践中，这意味着开发可以基于数据进行预测的计算机程序。就像人类可以从经验中学习一样，计算机也可以从经验中学习，数据就是经验。

A machine learning workflow is the process required for carrying out a machine learning project. Though individual projects can differ, most workflows share several common tasks: problem evaluation, data exploration, data preprocessing, model training/testing/deployment, etc. Below you’ll find helpful visualization of these core steps:

机器学习工作流程是执行机器学习项目所需的过程。尽管各个项目可能会有所不同，但是大多数工作流程共享一些共同的任务：问题评估，数据探索，数据预处理，模型训练/测试/部署等。在下面，您会发现这些核心步骤的有用可视化：

The ideal course introduces the entire process and provides interactive examples, assignments, and/or quizzes where students can perform each task themselves.

理想的课程介绍了整个过程，并提供了互动的示例，作业和/或测验，学生可以自己执行每个任务。

这些课程涵盖深度学习吗？ (Do these courses cover deep learning?)

First off, let’s define deep learning. Here is a succinct description:

首先，让我们定义深度学习。这是一个简洁的描述：

“Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.”

“深度学习是机器学习的一个子领域，涉及受大脑结构和功能启发的算法，称为人工神经网络。”

— Jason Brownlee from Machine Learning Mastery

—来自机器学习精通的 Jason Brownlee

As would be expected, portions of some of the machine learning courses contain deep learning content. I chose not to include deep learning-only courses, however. If you are interested in deep learning specifically, we’ve got you covered with the following article:

不出所料，某些机器学习课程的某些部分包含深度学习内容。但是，我选择不包括仅深度学习课程。如果您特别对深度学习感兴趣，我们将为您提供以下文章：

Dive into Deep Learning with 12 free online coursesEvery day brings new headlines for how deep learning is changing the world around us. A few examples:medium.freecodecamp.com

深入学习深度学习和12项免费在线课程， 每天都有新的头条新闻介绍深度学习如何改变我们周围的世界。 几个例子： medium.freecodecamp.com

My top three recommendations from that list would be:

从该列表中我最重要的三点建议是：

Creative Applications of Deep Learning with TensorFlow by Kadenze
使用TensorFlow进行深度学习的创新应用 由Kadenze
Neural Networks for Machine Learning by the University of Toronto (taught by Geoffrey Hinton) via Coursera
多伦多大学的 机器学习神经网络 (由Geoffrey Hinton教授)通过Coursera
Deep Learning A-Z™: Hands-On Artificial Neural Networks
深度学习AZ™：动手人工神经网络

Deep Learning A-Z™: Hands-On Artificial Neural Networksby Kirill Eremenko, Hadelin de Ponteves, and the SuperDataScience Team via Udemy
深度学习AZ™： Kirill Eremenko，Hadelin de Ponteves和SuperDataScience团队通过Udemy开发的 人工神经网络

建议的先决条件 (Recommended prerequisites)

Several courses listed below ask students to have prior programming, calculus, linear algebra, and statistics experience. These prerequisites are understandable given that machine learning is an advanced discipline.

下面列出的几门课程要求学生具有程序设计，微积分，线性代数和统计学的经验。考虑到机器学习是一门高级学科，这些前提条件是可以理解的。

Missing a few subjects? Good news! Some of this experience can be acquired through our recommendations in the first two articles (programming, statistics) of this Data Science Career Guide. Several top-ranked courses below also provide gentle calculus and linear algebra refreshers and highlight the aspects most relevant to machine learning for those less familiar.

缺少一些主题？好消息！可以从本《数据科学职业指南》的前两篇文章( 编程，统计 )中的建议中获得一些这种经验。以下几门排名最高的课程还提供了柔和的微积分和线性代数复习，并为那些不太熟悉的人强调了与机器学习最相关的方面。

我们选择的最佳机器学习课程是… (Our pick for the best machine learning course is…)

Machine Learning (Stanford University via Coursera)
机器学习 (通过Coursera的斯坦福大学)

Stanford University’s Machine Learning on Coursera is the clear current winner in terms of ratings, reviews, and syllabus fit. Taught by the famous Andrew Ng, Google Brain founder and former chief scientist at Baidu, this was the class that sparked the founding of Coursera. It has a 4.7-star weighted average rating over 422 reviews.

斯坦福大学在Coursera上的机器学习在评分，评论和课程大纲适合性方面显然是目前的赢家。该课程由著名的Google Brain创始人兼百度前首席科学家吴恩达(Andrew Ng)授课，该课程激发了Coursera的创立。它在422条评论中获得4.7星级加权平均评分。

Released in 2011, it covers all aspects of the machine learning workflow. Though it has a smaller scope than the original Stanford class upon which it is based, it still manages to cover a large number of techniques and algorithms. The estimated timeline is eleven weeks, with two weeks dedicated to neural networks and deep learning. Free and paid options are available.

它于2011年发布，涵盖了机器学习工作流程的所有方面。尽管它的范围比它所基于的原始斯坦福类的范围小，但它仍然设法涵盖了许多技术和算法。估计的时间表是十一周，其中两周专门用于神经网络和深度学习。提供免费和付费选项。

Ng is a dynamic yet gentle instructor with a palpable experience. He inspires confidence, especially when sharing practical implementation tips and warnings about common pitfalls. A linear algebra refresher is provided and Ng highlights the aspects of calculus most relevant to machine learning.

Ng是一位充满活力但又温柔的老师，经验丰富。他激发了人们的信心，尤其是在分享实用的实施技巧和有关常见陷阱的警告时。提供了线性代数复习器，Ng强调了与机器学习最相关的微积分方面。

Evaluation is automatic and is done via multiple choice quizzes that follow each lesson and programming assignments. The assignments (there are eight of them) can be completed in MATLAB or Octave, which is an open-source version of MATLAB. Ng explains his language choice:

评估是自动的，并且通过在每节课和编程作业之后进行的多项选择测验来完成。可以在MATLAB或Octave(MATLAB的开源版本)中完成分配(其中有八个)。 Ng解释了他的语言选择：

In the past, I’ve tried to teach machine learning using a large variety of different programming languages including C++, Java, Python, NumPy, and also Octave … And what I’ve seen after having taught machine learning for almost a decade is that you learn much faster if you use Octave as your programming environment.

过去，我曾尝试使用多种不同的编程语言(包括C ++，Java，Python，NumPy和Octave)来教授机器学习，而在教授机器学习近十年之后，我发现如果使用Octave作为编程环境，则学习速度会更快。

Though Python and R are likely more compelling choices in 2017 with the increased popularity of those languages, reviewers note that that shouldn’t stop you from taking the course.

尽管随着这些语言的日益普及， Python和R在2017年可能会成为更引人注目的选择，但审阅者注意到，这并不会阻止您选择这门课程。

A few prominent reviewers noted the following:

一些著名的评论者指出以下几点：

Of longstanding renown in the MOOC world, Stanford’s machine learning course really is the definitive introduction to this topic. The course broadly covers all of the major areas of machine learning … Prof. Ng precedes each segment with a motivating discussion and examples.

斯坦福大学的机器学习课程在MOOC世界中享有悠久的声誉，它确实是对该主题的权威介绍。该课程广泛涵盖了机器学习的所有主要领域……Ng教授在每个部分之前都进行了富有启发性的讨论和示例。

Of longstanding renown in the MOOC world, Stanford’s machine learning course really is the definitive introduction to this topic. The course broadly covers all of the major areas of machine learning … Prof. Ng precedes each segment with a motivating discussion and examples.

斯坦福大学的机器学习课程在MOOC世界中享有悠久的声誉，它确实是对该主题的权威介绍。该课程广泛涵盖了机器学习的所有主要领域……Ng教授在每个部分之前都进行了富有启发性的讨论和示例。

Andrew Ng is a gifted teacher and able to explain complicated subjects in a very intuitive and clear way, including the math behind all concepts. Highly recommended.

吴恩达(Andrew Ng)是一位天才的老师，能够以非常直观和清晰的方式解释复杂的主题，包括所有概念背后的数学知识。强烈推荐。

Andrew Ng is a gifted teacher and able to explain complicated subjects in a very intuitive and clear way, including the math behind all concepts. Highly recommended.

吴恩达(Andrew Ng)是一位有天赋的老师，能够以非常直观和清晰的方式讲解复杂的主题，包括所有概念背后的数学知识。强烈推荐。

常春藤盟校的新介绍，一位出色的教授 (A new Ivy League introduction with a brilliant professor)

Machine Learning (Columbia University via edX)
机器学习 (通过edX的哥伦比亚大学)

Columbia University’s Machine Learning is a relatively new offering that is part of their Artificial Intelligence MicroMasters on edX. Though it is newer and doesn’t have a large number of reviews, the ones that it does have are exceptionally strong. Professor John Paisley is noted as brilliant, clear, and clever. It has a 4.8-star weighted average rating over 10 reviews.

哥伦比亚大学的机器学习是相对较新的产品，属于edX上的人工智能MicroMaster的一部分。尽管它是更新的，并且没有大量评论，但它的确非常强大。约翰·佩斯利(John Paisley)教授被认为是杰出，清晰和聪明的。它在10条评论中拥有4.8星级加权平均评分。

The course also covers all aspects of the machine learning workflow and more algorithms than the above Stanford offering. Columbia’s is a more advanced introduction, with reviewers noting that students should be comfortable with the recommended prerequisites (calculus, linear algebra, statistics, probability, and coding).

该课程还涵盖了机器学习工作流程的所有方面，并提供了以上斯坦福大学提供的更多算法。 Columbia's是更高级的介绍，审阅者指出，学生应该对建议的先决条件(微积分，线性代数，统计，概率和编码)感到满意。

Quizzes (11), programming assignments (4), and a final exam are the modes of evaluation. Students can use either Python, Octave, or MATLAB to complete the assignments. The course’s total estimated timeline is eight to ten hours per week over twelve weeks. It is free with a verified certificate available for purchase.

测验(11)，编程作业(4)和期末考试是评估的模式。学生可以使用Python，Octave或MATLAB来完成作业。该课程的总时间表预计为十二周，每周八至十小时。它是免费的，并且可以购买经过验证的证书。

Below are a few of the aforementioned sparkling reviews:

以下是一些上述令人耳目一新的评论：

Over all my years of [being a] student I’ve come across professors who aren’t brilliant, professors who are brilliant but they don’t know how to explain the stuff clearly, and professors who are brilliant and know how explain the stuff clearly. Dr. Paisley belongs to the third group.

在成为一个学生的所有这些年中，我遇到过一些不那么出色的教授，那些虽然出色但他们不知道如何清楚地解释这些东西的教授，以及那些才华横溢并且知道如何解释这些东西的教授清楚地。佩斯利博士属于第三类。

Over all my years of [being a] student I’ve come across professors who aren’t brilliant, professors who are brilliant but they don’t know how to explain the stuff clearly, and professors who are brilliant and know how explain the stuff clearly. Dr. Paisley belongs to the third group.

在成为一个学生的所有这些年中，我遇到过一些不那么出色的教授，那些虽然出色但他们不知道如何清楚地解释这些东西的教授，以及那些才华横溢并且知道如何解释这些东西的教授清楚地。佩斯利博士属于第三类。

This is a great course … The instructor’s language is precise and that is, to my mind, one of the strongest points of the course. The lectures are of high quality and the slides are great too.

这是一门很棒的课程……讲师的语言很精确，在我看来，这是该课程最重要的方面之一。讲座质量很高，幻灯片也很棒。

This is a great course … The instructor’s language is precise and that is, to my mind, one of the strongest points of the course. The lectures are of high quality and the slides are great too.

这是一门很棒的课程……讲师的语言很精确，在我看来，这是该课程最重要的方面之一。讲座质量很高，幻灯片也很棒。

来自行业专家的Python＆R实用介绍 (A practical intro in Python & R from industry experts)

Machine Learning A-Z™: Hands-On Python & R In Data Science (Kirill Eremenko, Hadelin de Ponteves, and the SuperDataScience Team via Udemy)
机器学习AZ™：动手处理Python和R数据科学 (Kirill Eremenko，Hadelin de Ponteves和通过Udemy的SuperDataScience团队)

Machine Learning A-Z™ on Udemy is an impressively detailed offering that provides instruction in both Python and R, which is rare and can’t be said for any of the other top courses. It has a 4.5-star weighted average rating over 8,119 reviews, which makes it the most reviewed course of the ones considered.

Udemy上的Machine Learning AZ™是令人印象深刻的详细产品，它提供了 Python和R 两种语言的教学，这是很少见的，在其他任何顶级课程中都不能说。它拥有4.5颗星的加权平均评分，共有8,119条评论，这使其成为考虑次数最多的评论路线。

It covers the entire machine learning workflow and an almost ridiculous (in a good way) number of algorithms through 40.5 hours of on-demand video. The course takes a more applied approach and is lighter math-wise than the above two courses. Each section starts with an “intuition” video from Eremenko that summarizes the underlying theory of the concept being taught. de Ponteves then walks through implementation with separate videos for both Python and R.

通过40.5小时的点播视频，它涵盖了整个机器学习工作流程和几乎荒谬的(以一种很好的方式)数量的算法。与上述两个课程相比，本课程采用的是更实用的方法，并且在数学上较轻。每个部分都以Eremenko的“直觉”视频开头，总结了所教授概念的基本理论。然后，de Ponteves将针对Python和R的单独视频逐步实施。

As a “bonus,” the course includes Python and R code templates for students to download and use on their own projects. There are quizzes and homework challenges, though these aren’t the strong points of the course.

作为“奖励”，该课程包括Python和R代码模板，供学生下载并在自己的项目中使用。尽管不是课程的重点，但仍存在测验和作业挑战。

Eremenko and the SuperDataScience team are revered for their ability to “make the complex simple.” Also, the prerequisites listed are “just some high school mathematics,” so this course might be a better option for those daunted by the Stanford and Columbia offerings.

埃雷缅科(Eremenko)和SuperDataScience团队因“使复杂结构简单化”的能力而备受推崇。另外，列出的前提条件是“只是一些高中数学”，因此对于那些因斯坦福大学和哥伦比亚大学的课程而畏缩的人来说，本课程可能是一个更好的选择。

A few prominent reviewers noted the following:

一些著名的评论者指出以下几点：

The course is professionally produced, the sound quality is excellent, and the explanations are clear and concise … It’s an incredible value for your financial and time investment.

这门课程是专业制作的，音质出色，说明简洁明了……这对于您的财务和时间投资是不可思议的价值。

The course is professionally produced, the sound quality is excellent, and the explanations are clear and concise … It’s an incredible value for your financial and time investment.

这门课程是专业制作的，音质出色，说明简洁明了……这对于您的财务和时间投资是不可思议的价值。

It was spectacular to be able to follow the course in two different programming languages simultaneously.

能够同时使用两种不同的编程语言来学习这门课程，真是太了不起了。

It was spectacular to be able to follow the course in two different programming languages simultaneously.

能够同时使用两种不同的编程语言来学习这门课程，真是太了不起了。

竞赛 (The competition)

Our #1 pick had a weighted average rating of 4.7 out of 5 stars over 422 reviews. Let’s look at the other alternatives, sorted by descending rating. A reminder that deep learning-only courses are not included in this guide — you can find those here.

在422条评论中，我们的＃1选择加权平均评分为4.7，满分5星(4.7星)。让我们看看其他选择，按降序排列。提醒您，本指南未包含仅深度学习课程-您可以在此处找到。

The Analytics Edge (Massachusetts Institute of Technology/edX): More focused on analytics in general, though it does cover several machine learning topics. Uses R. Strong narrative that leverages familiar real-world examples. Challenging. Ten to fifteen hours per week over twelve weeks. Free with a verified certificate available for purchase. It has a 4.9-star weighted average rating over 214 reviews.

Analytics Edge (麻省理工学院/ edX)：尽管它涵盖了多个机器学习主题，但总体上更侧重于分析。使用R. Strong叙述，利用熟悉的真实示例。具有挑战性的。在十二周内每周十到十五小时。免费提供可购买的经过验证的证书。它在214条评论中获得4.9星级加权平均评分。

Python for Data Science and Machine Learning Bootcamp (Jose Portilla/Udemy): Has large chunks of machine learning content, but covers the whole data science process. More of a very detailed intro to Python. Amazing course, though not ideal for the scope of this guide. 21.5 hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.6-star weighted average rating over 3316 reviews.

用于数据科学和机器学习的Python训练营(Jose Portilla / Udemy)：具有大量的机器学习内容，但涵盖了整个数据科学过程。有关Python的非常详细的介绍。很棒的课程，尽管不是本指南范围的理想选择。 21.5小时的点播视频。成本因Udemy折扣而异，这是很常见的。它拥有3316条评论的4.6星级加权平均评分。

Data Science and Machine Learning Bootcamp with R (Jose Portilla/Udemy): The comments for Portilla’s above course apply here as well, except for R. 17.5 hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.6-star weighted average rating over 1317 reviews.

带有R的数据科学和机器学习训练营(Jose Portilla / Udemy)：对Portilla的上述课程的评论也适用于此，除了R. 17.5小时的点播视频。成本因Udemy折扣而异，这是很常见的。在1317条评论中，它具有4.6星级的加权平均评分。

Machine Learning Series (Lazy Programmer Inc./Udemy): Taught by a data scientist/big data engineer/full stack software engineer with an impressive resume, Lazy Programmer currently has a series of 16 machine learning-focused courses on Udemy. In total, the courses have 5000+ ratings and almost all of them have 4.6 stars. A useful course ordering is provided in each individual course’s description. Uses Python. Cost varies depending on Udemy discounts, which are frequent.

机器学习系列 (Lazy Programmer Inc./Udemy)：由数据科学家/大数据工程师/全栈软件工程师教授，履历令人印象深刻，Lazy Programmer目前在Udemy上开设了以机器学习为重点的16门课程。总的来说，这些课程具有5000多个评分，几乎所有课程均获得4.6星。每个课程的说明中都提供了有用的课程顺序。使用Python。成本因Udemy折扣而异，这是很常见的。

Machine Learning (Georgia Tech/Udacity): A compilation of what was three separate courses: Supervised, Unsupervised and Reinforcement Learning. Part of Udacity’s Machine Learning Engineer Nanodegree and Georgia Tech’s Online Master’s Degree (OMS). Bite-sized videos, as is Udacity’s style. Friendly professors. Estimated timeline of four months. Free. It has a 4.56-star weighted average rating over 9 reviews.

机器学习 (Georgia Tech / Udacity)：三门独立课程的汇编：监督学习，无监督学习和强化学习。 Udacity的机器学习工程师Nanodegree和Georgia Tech的在线硕士学位(OMS)的一部分。与Udacity的风格一样大小的视频。友好的教授。预计四个月的时间表。自由。它在9条评论中获得4.56星级加权平均评分。

Implementing Predictive Analytics with Spark in Azure HDInsight (Microsoft/edX): Introduces the core concepts of machine learning and a variety of algorithms. Leverages several big data-friendly tools, including Apache Spark, Scala, and Hadoop. Uses both Python and R. Four hours per week over six weeks. Free with a verified certificate available for purchase. It has a 4.5-star weighted average rating over 6 reviews.

在Azure HDInsight (Microsoft / edX)中使用Spark实施预测分析：介绍机器学习和各种算法的核心概念。利用几个大数据友好的工具，包括Apache Spark，Scala和Hadoop。同时使用Python和R。在六个星期内每周四个小时。免费提供可购买的经过验证的证书。它有超过6条评论的4.5星级加权平均评分。

Data Science and Machine Learning with Python — Hands On! (Frank Kane/Udemy): Uses Python. Kane has nine years of experience at Amazon and IMDb. Nine hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.5-star weighted average rating over 4139 reviews.

使用Python进行数据科学和机器学习-动手！ (Frank Kane / Udemy)：使用Python。凯恩(Kane)在亚马逊和IMDb方面拥有九年的经验。九小时的点播视频。成本因Udemy折扣而异，这是很常见的。它有超过4139条评论的4.5星级加权平均评分。

Scala and Spark for Big Data and Machine Learning (Jose Portilla/Udemy): “Big data” focus, specifically on implementation in Scala and Spark. Ten hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.5-star weighted average rating over 607 reviews.

适用于大数据和机器学习的Scala和Spark (Jose Portilla / Udemy)：“大数据”侧重于Scala和Spark的实现。十小时的点播视频。成本因Udemy折扣而异，这是很常见的。它拥有607条评论的4.5星级加权平均评分。

Machine Learning Engineer Nanodegree (Udacity): Udacity’s flagship Machine Learning program, which features a best-in-class project review system and career support. The program is a compilation of several individual Udacity courses, which are free. Co-created by Kaggle. Estimated timeline of six months. Currently costs $199 USD per month with a 50% tuition refund available for those who graduate within 12 months. It has a 4.5-star weighted average rating over 2 reviews.

纳米级机器学习工程师 (Udacity)：Udacity的旗舰机器学习计划，具有一流的项目审查系统和职业支持。该程序是一些免费的Udacity课程的汇编。由Kaggle共同创建。预计六个月的时间表。目前的费用为每月199美元，适用于在12个月内毕业的学生，可获得50％的学费退款。该酒店在2条评论中获得4.5星级加权平均评分。

Learning From Data (Introductory Machine Learning) (California Institute of Technology/edX): Enrollment is currently closed on edX, but is also available via CalTech’s independent platform (see below). It has a 4.49-star weighted average rating over 42 reviews.

从数据学习(机器学习入门) (加利福尼亚理工学院/ edX)：edX目前已关闭注册，但也可以通过CalTech的独立平台进行注册(请参见下文)。它有超过42条评论的4.49星级加权平均评分。

Learning From Data (Introductory Machine Learning) (Yaser Abu-Mostafa/California Institute of Technology): “A real Caltech course, not a watered-down version.” Reviews note it is excellent for understanding machine learning theory. The professor, Yaser Abu-Mostafa, is popular among students and also wrote the textbook upon which this course is based. Videos are taped lectures (with lectures slides picture-in-picture) uploaded to YouTube. Homework assignments are .pdf files. The course experience for online students isn’t as polished as the top three recommendations. It has a 4.43-star weighted average rating over 7 reviews.

从数据中学习(机器学习入门) (Yaser Abu-Mostafa /加利福尼亚理工学院)：“一门真正的Caltech课程，而不是精简版。” 评论指出，它对于理解机器学习理论非常有用。教授Yaser Abu-Mostafa在学生中很受欢迎，并撰写了本课程所基于的教科书。视频是录制的演讲(带有幻灯片的画中画)，已上传到YouTube。作业是.pdf文件。在线学生的课程体验没有前三项建议那么完善。它在7条评论中获得4.43星级加权平均评分。

Mining Massive Datasets (Stanford University): Machine learning with a focus on “big data.” Introduces modern distributed file systems and MapReduce. Ten hours per week over seven weeks. Free. It has a 4.4-star weighted average rating over 30 reviews.

挖掘海量数据集 (斯坦福大学)：以“大数据”为重点的机器学习。引入了现代的分布式文件系统和MapReduce。在七个星期内，每周十小时。自由。它拥有超过30条评论的4.4星级加权平均评分。

AWS Machine Learning: A Complete Guide With Python (Chandra Lingam/Udemy): A unique focus on cloud-based machine learning and specifically Amazon Web Services. Uses Python. Nine hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.4-star weighted average rating over 62 reviews.

AWS机器学习：Python完整指南 (Chandra Lingam / Udemy)：专门关注基于云的机器学习，尤其是Amazon Web Services。使用Python。九小时的点播视频。成本因Udemy折扣而异，这是很常见的。它有超过62条评论的4.4星级加权平均评分。

Introduction to Machine Learning & Face Detection in Python (Holczer Balazs/Udemy): Uses Python. Eight hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.4-star weighted average rating over 162 reviews.

Python机器学习和面部检测简介 (Holczer Balazs / Udemy)：使用Python。八个小时的点播视频。成本因Udemy折扣而异，这是很常见的。它拥有162条评论的4.4星级加权平均评分。

StatLearning: Statistical Learning (Stanford University): Based on the excellent textbook, “An Introduction to Statistical Learning, with Applications in R” and taught by the professors who wrote it. Reviewers note that the MOOC isn’t as good as the book, citing “thin” exercises and mediocre videos. Five hours per week over nine weeks. Free. It has a 4.35-star weighted average rating over 84 reviews.

StatLearning：统计学习 (斯坦福大学)：基于出色的教科书“ R的应用中的统计学习入门 ”，并由撰写此书的教授教授。审稿人注意到，MOOC并不如这本书那样好，它引用了“App.svelte”练习和平庸的视频。在九周内，每周五小时。自由。该酒店在84条评论中给出了4.35星级的加权平均评分。

Machine Learning Specialization (University of Washington/Coursera): Great courses, but last two classes (including the capstone project) were canceled. Reviewers note that this series is more digestable (read: easier for those without strong technical backgrounds) than other top machine learning courses (e.g. Stanford’s or Caltech’s). Be aware that the series is incomplete with recommender systems, deep learning, and a summary missing. Free and paid options available. It has a 4.31-star weighted average rating over 80 reviews.

机器学习专业 (华盛顿大学/库塞拉大学)：很棒的课程，但最后两节课(包括顶石项目)被取消。审阅者注意到，该系列比其他顶级机器学习课程(例如，斯坦福大学或加州理工学院)更容易理解(阅读：对于没有扎实的技术背景的人来说更容易)。请注意，该系列不完整，包括推荐系统，深度学习和摘要。提供免费和付费选项。它拥有80则评论中的4.31星级加权平均评分。

From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase (Loony Corn/Udemy): “A down-to-earth, shy but confident take on machine learning techniques.” Taught by four-person team with decades of industry experience together. Uses Python. Cost varies depending on Udemy discounts, which are frequent. It has a 4.2-star weighted average rating over 494 reviews.

从0到1：机器学习，NLP和Python切入正题(Loony Corn / Udemy)：“脚踏实地，害羞但自信的机器学习技术。” 由具有数十年行业经验的四人团队共同授课。使用Python。成本因Udemy折扣而异，这是很常见的。它拥有494条评论中的4.2星级加权平均评分。

Principles of Machine Learning (Microsoft/edX): Uses R, Python, and Microsoft Azure Machine Learning. Part of the Microsoft Professional Program Certificate in Data Science. Three to four hours per week over six weeks. Free with a verified certificate available for purchase. It has a 4.09-star weighted average rating over 11 reviews.

机器学习原理 (Microsoft / edX)：使用R，Python和Microsoft Azure机器学习。数据科学Microsoft专业计划证书的一部分。六周内每周三到四个小时。免费提供可购买的经过验证的证书。在11条评论中获得4.09星级加权平均评分。

Big Data: Statistical Inference and Machine Learning (Queensland University of Technology/FutureLearn): A nice, brief exploratory machine learning course with a focus on big data. Covers a few tools like R, H2O Flow, and WEKA. Only three weeks in duration at a recommended two hours per week, but one reviewer noted that six hours per week would be more appropriate. Free and paid options available. It has a 4-star weighted average rating over 4 reviews.

大数据：统计推断和机器学习 (昆士兰科技大学/ FutureLearn)：不错的，简短的探索性机器学习课程，重点是大数据。涵盖了一些工具，例如R，H2O Flow和WEKA。持续时间只有三周，建议每周两小时，但是一位评论者指出，每周六小时更为合适。提供免费和付费选项。它具有4条评论的4星级加权平均评分。

Genomic Data Science and Clustering (Bioinformatics V) (University of California, San Diego/Coursera): For those interested in the intersection of computer science and biology and how it represents an important frontier in modern science. Focuses on clustering and dimensionality reduction. Part of UCSD’s Bioinformatics Specialization. Free and paid options available. It has a 4-star weighted average rating over 3 reviews.

基因组数据科学与聚类 (生物信息学V)(加利福尼亚大学圣地亚哥分校/库塞拉分校)：对于那些对计算机科学与生物学的交汇及其如何代表现代科学的重要前沿领域感兴趣的人。专注于聚类和降维。 UCSD的生物信息学专业化的一部分。提供免费和付费选项。它具有3条评论的4星级加权平均评分。

Intro to Machine Learning (Udacity): Prioritizes topic breadth and practical tools (in Python) over depth and theory. The instructors, Sebastian Thrun and Katie Malone, make this class so fun. Consists of bite-sized videos and quizzes followed by a mini-project for each lesson. Currently part of Udacity’s Data Analyst Nanodegree. Estimated timeline of ten weeks. Free. It has a 3.95-star weighted average rating over 19 reviews.

机器学习入门 (Udacity)：优先考虑主题的广度和实用工具(在Python中)，而不是深度和理论。塞巴斯蒂安·特伦(Sebastian Thrun)和凯蒂·马龙(Katie Malone)的老师让这堂课很有趣。包括一口大小的视频和测验，以及每节课的一个微型项目。目前是Udacity的数据分析师纳米学位的一部分。预计时间表为十周。自由。它有超过19条评论的3.95星级加权平均评分。

Machine Learning for Data Analysis (Wesleyan University/Coursera): A brief intro machine learning and a few select algorithms. Covers decision trees, random forests, lasso regression, and k-means clustering. Part of Wesleyan’s Data Analysis and Interpretation Specialization. Estimated timeline of four weeks. Free and paid options available. It has a 3.6-star weighted average rating over 5 reviews.

用于数据分析的机器学习 (卫斯理大学/库塞拉)：简短的机器学习入门和一些精选算法。涵盖决策树，随机森林，套索回归和k-均值聚类。卫斯理数据分析和口译专业化的一部分。预计时间表为四个星期。提供免费和付费选项。它有超过5条评论的3.6星级加权平均评分。

Programming with Python for Data Science (Microsoft/edX): Produced by Microsoft in partnership with Coding Dojo. Uses Python. Eight hours per week over six weeks. Free and paid options available. It has a 3.46-star weighted average rating over 37 reviews.

使用Python进行数据科学编程 (Microsoft / edX)：由Microsoft与Coding Dojo合作生产。使用Python。在六个星期内，每周八小时。提供免费和付费选项。它在37条评论中拥有3.46颗星的加权平均评分。

Machine Learning for Trading (Georgia Tech/Udacity): Focuses on applying probabilistic machine learning approaches to trading decisions. Uses Python. Part of Udacity’s Machine Learning Engineer Nanodegree and Georgia Tech’s Online Master’s Degree (OMS). Estimated timeline of four months. Free. It has a 3.29-star weighted average rating over 14 reviews.

用于交易的机器学习 (Georgia Tech / Udacity)：专注于将概率机器学习方法应用于交易决策。使用Python。 Udacity的机器学习工程师Nanodegree和Georgia Tech的在线硕士学位(OMS)的一部分。预计四个月的时间表。自由。它在14条评论中拥有3.29星级加权平均评分。

Practical Machine Learning (Johns Hopkins University/Coursera): A brief, practical introduction to a number of machine learning algorithms. Several one/two-star reviews expressing a variety of concerns. Part of JHU’s Data Science Specialization. Four to nine hours per week over four weeks. Free and paid options available. It has a 3.11-star weighted average rating over 37 reviews.

实用机器学习 (Johns Hopkins University / Coursera)：对许多机器学习算法的简要而实用的介绍。几则一/二星级评论表达了各种各样的担忧。 JHU的数据科学专业化的一部分。在四个星期内每周四至九小时。提供免费和付费选项。它在37条评论中获得3.11星级加权平均评分。

Machine Learning for Data Science and Analytics (Columbia University/edX): Introduces a wide range of machine learning topics. Some passionate negative reviews with concerns including content choices, a lack of programming assignments, and uninspiring presentation. Seven to ten hours per week over five weeks. Free with a verified certificate available for purchase. It has a 2.74-star weighted average rating over 36 reviews.

数据科学和分析的机器学习 (哥伦比亚大学/ edX)：介绍了广泛的机器学习主题。一些充满激情的负面评论，涉及的内容包括内容选择，缺少编程任务以及鼓舞人心的演讲。五周内每周七至十小时。免费提供可购买的经过验证的证书。它拥有超过36条评论的2.74星级加权平均评分。

Recommender Systems Specialization (University of Minnesota/Coursera): Strong focus one specific type of machine learning — recommender systems. A four course specialization plus a capstone project, which is a case study. Taught using LensKit (an open-source toolkit for recommender systems). Free and paid options available. It has a 2-star weighted average rating over 2 reviews.

推荐系统专业化 (明尼苏达大学/库塞拉大学)：重点关注一种特定类型的机器学习-推荐系统。一个四门课程的专业化加上一个顶点项目，这是一个案例研究。使用LensKit(推荐系统的开源工具包)进行教学。提供免费和付费选项。该酒店在2条评论中拥有2星级加权平均评分。

Machine Learning With Big Data (University of California, San Diego/Coursera): Terrible reviews that highlight poor instruction and evaluation. Some noted it took them mere hours to complete the whole course. Part of UCSD’s Big Data Specialization. Free and paid options available. It has a 1.86-star weighted average rating over 14 reviews.

具有大数据的机器学习 (加利福尼亚大学，圣地亚哥/库尔塞拉)：糟糕的评语强调了糟糕的指导和评估。一些人指出，他们只花了几个小时就完成了整个课程。 UCSD大数据专业化的一部分。提供免费和付费选项。它在14条评论中拥有1.86星级加权平均评分。

Practical Predictive Analytics: Models and Methods (University of Washington/Coursera): A brief intro to core machine learning concepts. One reviewer noted that there was a lack of quizzes and that the assignments were not challenging. Part of UW’s Data Science at Scale Specialization. Six to eight hours per week over four weeks. Free and paid options available. It has a 1.75-star weighted average rating over 4 reviews.

实用的预测分析：模型和方法 (华盛顿大学/库塞拉大学)：核心机器学习概念的简要介绍。一位审阅者指出，没有测验，作业也没有挑战性。华盛顿大学数据科学专业的一部分。在四个星期内每周六至八个小时。提供免费和付费选项。它在4条评论中拥有1.75星级加权平均评分。

The following courses had one or no reviews as of May 2017.

截至2017年5月，以下课程没有评论或没有评论。

Machine Learning for Musicians and Artists (Goldsmiths, University of London/Kadenze): Unique. Students learn algorithms, software tools, and machine learning best practices to make sense of human gesture, musical audio, and other real-time data. Seven sessions in length. Audit (free) and premium ($10 USD per month) options available. It has one 5-star review.

面向音乐家和艺术家的机器学习 (伦敦大学/ Kadenze的金史密斯学院)：独特。学生学习算法，软件工具和机器学习最佳实践，以理解人的手势，音乐音频和其他实时数据。七节课。提供审计(免费)和高级(每月10美元)选项。它具有1星级。

Applied Machine Learning in Python (University of Michigan/Coursera): Taught using Python and the scikit learn toolkit. Part of the Applied Data Science with Python Specialization. Scheduled to start May 29th. Free and paid options available.

Python中的应用机器学习 (密歇根大学/库尔塞拉分校)：使用Python和scikit学习工具包进行授课。 Python专业化应用数据科学的一部分。计划于5月29日开始。提供免费和付费选项。

Applied Machine Learning (Microsoft/edX): Taught using various tools, including Python, R, and Microsoft Azure Machine Learning (note: Microsoft produces the course). Includes hands-on labs to reinforce the lecture content. Three to four hours per week over six weeks. Free with a verified certificate available for purchase.

应用机器学习 (Microsoft / edX)：使用各种工具进行授课，包括Python，R和Microsoft Azure机器学习(注意：Microsoft开设课程)。包括动手实验，以加强讲座内容。六周内每周三到四个小时。免费提供可购买的经过验证的证书。

Machine Learning with Python (Big Data University): Taught using Python. Targeted towards beginners. Estimated completion time of four hours. Big Data University is affiliated with IBM. Free.

使用Python进行机器学习 (大数据大学)：使用Python进行授课。针对初学者。估计完成时间为四个小时。大数据大学隶属于IBM。自由。

Machine Learning with Apache SystemML (Big Data University): Taught using Apache SystemML, which is a declarative style language designed for large-scale machine learning. Estimated completion time of eight hours. Big Data University is affiliated with IBM. Free.

使用Apache SystemML (大数据大学)进行机器学习：使用Apache SystemML进行讲授，Apache SystemML是为大规模机器学习而设计的声明式语言。估计完成时间为八小时。大数据大学隶属于IBM。自由。

Machine Learning for Data Science (University of California, San Diego/edX): Doesn’t launch until January 2018. Programming examples and assignments are in Python, using Jupyter notebooks. Eight hours per week over ten weeks. Free with a verified certificate available for purchase.

数据科学机器学习 (加利福尼亚大学，圣地亚哥/ edX)：直到2018年1月才启动。编程示例和赋值是使用Jupyter笔记本使用Python编写的。十周内每周八小时。免费提供可购买的经过验证的证书。

Introduction to Analytics Modeling (Georgia Tech/edX): The course advertises R as its primary programming tool. Five to ten hours per week over ten weeks. Free with a verified certificate available for purchase.

分析建模简介 (Georgia Tech / edX)：该课程宣传R作为其主要的编程工具。十周内每周五至十小时。免费提供可购买的经过验证的证书。

Predictive Analytics: Gaining Insights from Big Data (Queensland University of Technology/FutureLearn): Brief overview of a few algorithms. Uses Hewlett Packard Enterprise’s Vertica Analytics platform as an applied tool. Start date to be announced. Two hours per week over four weeks. Free with a Certificate of Achievement available for purchase.

预测分析：从大数据中获取见解 (昆士兰科技大学/ FutureLearn)：几种算法的简要概述。使用Hewlett Packard Enterprise的Vertica Analytics平台作为应用工具。开始日期待定。每周两小时，持续四个星期。免费提供成就证书供购买。

Introducción al Machine Learning (Universitas Telefónica/Miríada X): Taught in Spanish. An introduction to machine learning that covers supervised and unsupervised learning. A total of twenty estimated hours over four weeks.

机器学习入门 (UniversitasTelefónica/MiríadaX)：西班牙语授课。机器学习简介，涵盖有监督和无监督学习。在四个星期内总共估计了二十个小时。

Machine Learning Path Step (Dataquest): Taught in Python using Dataquest’s interactive in-browser platform. Multiple guided projects and a “plus” project where you build your own machine learning system using your own data. Subscription required.

机器学习路径步骤 (Dataquest)：使用Dataquest的交互式浏览器内平台进行Python授课。多个指导项目和一个“加号”项目，您可以在其中使用自己的数据构建自己的机器学习系统。需要订阅。

The following six courses are offered by DataCamp. DataCamp’s hybrid teaching style leverages video and text-based instruction with lots of examples through an in-browser code editor. A subscription is required for full access to each course.

DataCamp提供以下六门课程。 DataCamp的混合教学风格通过浏览器内代码编辑器利用基于视频和文本的教学以及大量示例。要完全访问每门课程，需要订阅。

Introduction to Machine Learning (DataCamp): Covers classification, regression, and clustering algorithms. Uses R. Fifteen videos and 81 exercises with an estimated timeline of six hours.

机器学习简介 (DataCamp)：涵盖分类，回归和聚类算法。使用R. 15个视频和81个练习，估计时间表为6个小时。

Supervised Learning with scikit-learn (DataCamp): Uses Python and scikit-learn. Covers classification and regression algorithms. Seventeen videos and 54 exercises with an estimated timeline of four hours.

使用scikit-learn (DataCamp)进行监督学习：使用Python和scikit-learn。涵盖分类和回归算法。 17个视频和54个练习，估计时间为4个小时。

Unsupervised Learning in R (DataCamp): Provides a basic introduction to clustering and dimensionality reduction in R. Sixteen videos and 49 exercises with an estimated timeline of four hours.

R中的无监督学习 (DataCamp)：提供有关R中的聚类和降维的基本介绍。16个视频和49个练习，估计时间为4个小时。

Machine Learning Toolbox (DataCamp): Teaches the “big ideas” in machine learning. Uses R. 24 videos and 88 exercises with an estimated timeline of four hours.

机器学习工具箱 (DataCamp)：教授机器学习中的“大创意”。使用R. 24个视频和88个练习，估计时间表为四个小时。

Machine Learning with the Experts: School Budgets (DataCamp): A case study from a machine learning competition on DrivenData. Involves building a model to automatically classify items in a school’s budget. DataCamp’s “Supervised Learning with scikit-learn” is a prerequisite. Fifteen videos and 51 exercises with an estimated timeline of four hours.

与专家一起进行机器学习：学校预算 (DataCamp)：来自DrivenData机器学习竞赛的案例研究。涉及建立模型以自动对学校预算中的项目进行分类。先决条件是DataCamp的“使用scikit-learn进行监督学习”。 15个视频和51个练习，估计时间为4个小时。

Unsupervised Learning in Python (DataCamp): Covers a variety of unsupervised learning algorithms using Python, scikit-learn, and scipy. The course ends with students building a recommender system to recommend popular musical artists. Thirteen videos and 52 exercises with an estimated timeline of four hours.

Python中的无监督学习 (DataCamp)：涵盖了使用Python，scikit-learn和scipy进行的各种无监督学习算法。课程以学生建立推荐系统来推荐流行音乐艺术家为结尾。十三个视频和52个练习，估计时间为四个小时。

Machine Learning (Tom Mitchell/Carnegie Mellon University): Carnegie Mellon’s graduate introductory machine learning course. A prerequisite to their second graduate level course, “Statistical Machine Learning.” Taped university lectures with practice problems, homework assignments, and a midterm (all with solutions) posted online. A 2011 version of the course also exists. CMU is one of the best graduate schools for studying machine learning and has a whole department dedicated to ML. Free.

机器学习 (汤姆·米切尔/卡内基·梅隆大学)：卡内基·梅隆大学的研究生机器学习入门课程。他们的第二个研究生课程“统计机器学习”的前提条件。录制大学演讲，内容包括练习题，家庭作业和在线发布的期中考试(均附有解决方案)。还存在该课程的2011年版本。 CMU是研究机器学习的最好的研究生院之一，并设有专门负责ML的整个系。自由。

Statistical Machine Learning (Larry Wasserman/Carnegie Mellon University): Likely the most advanced course in this guide. A follow-up to Carnegie Mellon’s Machine Learning course. Taped university lectures with practice problems, homework assignments, and a midterm (all with solutions) posted online. Free.

统计机器学习 (拉里·瓦瑟曼/卡内基梅隆大学)：可能是本指南中最高级的课程。卡内基梅隆大学机器学习课程的后续课程。录制大学演讲，内容包括练习题，家庭作业和在线发布的期中考试(均附有解决方案)。自由。

Undergraduate Machine Learning (Nando de Freitas/University of British Columbia): An undergraduate machine learning course. Lectures are filmed and put on YouTube with the slides posted on the course website. The course assignments are posted as well (no solutions, though). de Freitas is now a full-time professor at the University of Oxford and receives praise for his teaching abilities in various forums. Graduate version available (see below).

本科机器学习 (Nando de Freitas /不列颠哥伦比亚大学)：本科机器学习课程。讲座被录制并放在课程网站上，并放到YouTube上。课程作业也会发布(但没有解决方案)。 de Freitas现在是牛津大学的专职教授，并因其在各种论坛上的教学能力而受到赞誉。提供研究生版本(请参见下文)。

Machine Learning (Nando de Freitas/University of British Columbia): A graduate machine learning course. The comments in de Freitas’ undergraduate course (above) apply here as well.

机器学习 (Nando de Freitas /不列颠哥伦比亚大学)：研究生机器学习课程。 de Freitas本科课程(以上)中的评论也适用于此。

结语 (Wrapping it Up)

This is the fifth of a six-piece series that covers the best online courses for launching yourself into the data science field. We covered programming in the first article, statistics and probability in the second article, intros to data science in the third article, and data visualization in the fourth.

这是一个由六部分组成的系列文章的第五篇，该系列文章介绍了将自己入门到数据科学领域的最佳在线课程。我们在第一篇文章中介绍了编程，在第二篇文章中介绍了统计和概率，在第三篇文章中介绍了数据科学，在第四篇文章中介绍了数据可视化。

I ranked every Intro to Data Science course on the internet, based on thousands of data pointsA year ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data…

我根据数千个数据点对互联网上的每门数据科学入门课程进行排名。 一年前，我退出了加拿大最好的计算机科学程序之一。 我开始创建自己的数据…

The final piece will be a summary of those articles, plus the best online courses for other key topics such as data wrangling, databases, and even software engineering.

If you’re looking for a complete list of Data Science online courses, you can find them on Class Central’s Data Science and Big Data subject page.

If you're looking for a complete list of Data Science online courses, you can find them on Class Central's Data Science and Big Data subject page.

If you enjoyed reading this, check out some of Class Central’s other pieces:

If you enjoyed reading this, check out some of Class Central 's other pieces:

Here are 250 Ivy League courses you can take online right now for free250 MOOCs from Brown, Columbia, Cornell, Dartmouth, Harvard, Penn, Princeton, and Yale.

Here are 250 Ivy League courses you can take online right now for free 250 MOOCs from Brown, Columbia, Cornell, Dartmouth, Harvard, Penn, Princeton, and Yale.

The 50 best free online university courses according to dataWhen I launched Class Central back in November 2011, there were around 18 or so free online courses, and almost all of…

The 50 best free online university courses according to data When I launched Class Central back in November 2011, there were around 18 or so free online courses, and almost all of…

If you have suggestions for courses I missed, let me know in the responses!

If you found this helpful, click the ? so more people will see it here on Medium.

This is a condensed version of my original article published on Class Central, where I’ve included detailed course syllabi.

This is a condensed version of my original article published on Class Central, where I've included detailed course syllabi.

翻译自: https://www.freecodecamp.org/news/every-single-machine-learning-course-on-the-internet-ranked-by-your-reviews-3c4a7b8026c0/