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What are MOOCs?

How do I register?

To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.

How do these MOOCs or free online courses work?

MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.

8 out of 8 people found the following review useful

2 years ago

Udacity's Intro to Machine Learning is an introduction to data analysis using Python and the sklearn package. The course consists of 15 lessons covering a wide range of machine learning topics including classification algorithms (Naive Bayes, decision trees and SVMs), linear regression, clustering, selecting and transf
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Udacity's Intro to Machine Learning is an introduction to data analysis using Python and the sklearn package. The course consists of 15 lessons covering a wide range of machine learning topics including classification algorithms (Naive Bayes, decision trees and SVMs), linear regression, clustering, selecting and transforming features and validation. As a self-paced course, you can take however long you wish on each lesson; some take less than an hour, while others can take several hours depending on how long you work on the mini projects. Intro to Machine Learning requires basic programming and math skills.

Each lesson consists of a series of video segments and quizzes introducing a new topic followed by a mini-project that gives you a chance to work with code implementing the topics you learned in Python using scikit-learn. The course instructors Katie and Sebastian (the guy who runs Udacity) do a good job explaining the material keeping the course engaging, but they keep things simple. The quizzes, at times, are almost patronizingly easy. The mini projects are a bit harder and contribute more to learning, although they occasionally lack adequate guidance and feedback to help students arrive at the expected output. The final project and many of the mini-projects leading up to it, involve detecting persons of interest in the Enron scandal using a data set of emails sent by Enron employees. Interesting real-world data sets are always a plus.

Intro to Machine Learning is an accessible first course in machine learning that prioritizes breadth, high level understanding and practical tools over depth and theory. You won't be an expert in any of the topics covered in this course by the time you're done, but you will be exposed to several major topics in machine learning and have a basic understanding of how they work. If you are interested taking a similar course with many interesting mini projects that uses the R programming language, try MIT's Analytics Edge on edX. Coursera's Machine Learning with Andrew Ng is a logical next step to dig deeper into machine learning algorithm design and implementation, while Caltech's Learning from Data on edX is a great course if you are interested in machine learning theory. Just be aware that both of these courses (particularly the Caltech course) require a stronger math background.

I give this course 4 out of 5 stars: Very Good.

Each lesson consists of a series of video segments and quizzes introducing a new topic followed by a mini-project that gives you a chance to work with code implementing the topics you learned in Python using scikit-learn. The course instructors Katie and Sebastian (the guy who runs Udacity) do a good job explaining the material keeping the course engaging, but they keep things simple. The quizzes, at times, are almost patronizingly easy. The mini projects are a bit harder and contribute more to learning, although they occasionally lack adequate guidance and feedback to help students arrive at the expected output. The final project and many of the mini-projects leading up to it, involve detecting persons of interest in the Enron scandal using a data set of emails sent by Enron employees. Interesting real-world data sets are always a plus.

Intro to Machine Learning is an accessible first course in machine learning that prioritizes breadth, high level understanding and practical tools over depth and theory. You won't be an expert in any of the topics covered in this course by the time you're done, but you will be exposed to several major topics in machine learning and have a basic understanding of how they work. If you are interested taking a similar course with many interesting mini projects that uses the R programming language, try MIT's Analytics Edge on edX. Coursera's Machine Learning with Andrew Ng is a logical next step to dig deeper into machine learning algorithm design and implementation, while Caltech's Learning from Data on edX is a great course if you are interested in machine learning theory. Just be aware that both of these courses (particularly the Caltech course) require a stronger math background.

I give this course 4 out of 5 stars: Very Good.

11 months ago
**partially completed** this course.

I started this course after having taken the Coursera course of AndrewNg. My goal was to apply the algorithms in Python and to become familiar with Scikit learn. I have completed about 70% of Udacities intro to ML and I have to say I am very disappointed about the quality of the course, especially about the quality of
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I started this course after having taken the Coursera course of AndrewNg. My goal was to apply the algorithms in Python and to become familiar with Scikit learn. I have completed about 70% of Udacities intro to ML and I have to say I am very disappointed about the quality of the course, especially about the quality of the videos and the quizzes. The mathematical level is broken down to high school level, which is good for the intuitive understanding, but in my opinion the level is far too low to learn anything serious, especially when comparing with AndrewNgs course. The same applies for the quizzes. Let me illustrate this with an example. Assume they want you to calculate a*b/(c*d+e*f). Then there would be a quiz to calculate a*b, another quiz to calculate c*d, another quiz to calculate e*f, another quiz to calculate c*d+e*f, and then finally the whole thing. One has to go through 6 videos and 5 quizzes to calculate a simple fraction. The programming assignments are similar in quality. I have to say I didnt finish the course and therefore I can not comment on the final project, which may be more serious. In conclucion, I can not recommend this course to anyone who has a serious interest in learning something about ML. Invest your time better!!

7 months ago
**partially completed** this course.

This is practical course, instructors are nice. If you like python you would love this course. Mathematics is not strong here but this an Intro to Machine learning and they are doing the best they can to expose us not only to machine learning algorithm but sci-kit learn api which keeps you hooked on this course. Once
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This is practical course, instructors are nice. If you like python you would love this course. Mathematics is not strong here but this an Intro to Machine learning and they are doing the best they can to expose us not only to machine learning algorithm but sci-kit learn api which keeps you hooked on this course. Once you get the idea of any algorithm you can go deeper into mathematical aspects of it. One of the issue I faced was the problem with quizzes few often they are a little opaque.

1 out of 1 people found the following review useful

a year ago
**completed** this course.

I hated how the quiz questions weren't clearly written out (some missing information was said instead of shown visually). This stops you from skimming through the quizzes if you are already familiar with the concepts.

1 out of 1 people found the following review useful

3 years ago

Nice for a beginner who just wants an intro to machine learning and not delve deeper into the implementation and mathematics behind the algorithms.

5 months ago
**partially completed** this course.

The math is sloppy and confusing. It often seems like he can't quite decide what he's asking for the probability of. Even worse, the expressions will suddenly change between slides with no explanation of why. In an attempt to simplify the math, they just muddle it up.
I'm not sure who the intended audience is for this
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The math is sloppy and confusing. It often seems like he can't quite decide what he's asking for the probability of. Even worse, the expressions will suddenly change between slides with no explanation of why. In an attempt to simplify the math, they just muddle it up.

I'm not sure who the intended audience is for this course. It's conceptually too slow for anyone with sufficient background to do the math. Yet the math is almost unrecognizable to anyone who already knows it

Unfortunately, this is a lot of like other Udacity courses, that try too hard to be fun, and fail to be sufficiently substantive.

On a positive note, the Python examples are good.

I'm not sure who the intended audience is for this course. It's conceptually too slow for anyone with sufficient background to do the math. Yet the math is almost unrecognizable to anyone who already knows it

Unfortunately, this is a lot of like other Udacity courses, that try too hard to be fun, and fail to be sufficiently substantive.

On a positive note, the Python examples are good.

4 months ago
**audited** this course and found the course difficulty to be **easy**.

The course will teach you the very basics of sklearn but not much of machine learning. Some core concepts are explained in an easy way. The quizzes are however sometime next to idiotic. It would be better to drop half of them altogether.

I gave it 4 because I did not know neither python nor sklearn and it was useful for me. If you know python then go somewhere else.

I gave it 4 because I did not know neither python nor sklearn and it was useful for me. If you know python then go somewhere else.

7 months ago

Nice, intuitive introduction for a beginner. It is mostly practical, the math is very shallow so if you are interested in the math behind it, you won't be interested in the course.

0 out of 1 people found the following review useful

0 out of 1 people found the following review useful