Introduction to Machine Learning

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.

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!!

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.

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.

It’s so cringe worthy, I couldn’t get past the first couple of sections. This is supposed to be a foundation for people wanting to pay to take the data science nanodegree. It’s as of they’re just not tskkmg it seriously at all. Painful to watch. Having completed and enjoyed the data analyst nanodegree, this has put me off further study with Udacity.