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Introduction to Machine Learning

Introduction to Machine Learning


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8.1/10 (Our Score)
Product is rated as #261 in category Machine Learning

Want to learn how to analyze the huge amounts of data? In this course you will learn modern methods of machine learning to help you choose the right methods to analyze your data and interpret the results correctly. This course is an introduction to machine learning. It will cover the modern methods of statistics and machine learning as well as mathematical prerequisites for them. We will discuss the methods used in classification and clustering problems. You will learn different regression methods. Various examples and different software applications are considered in the course. You will get not only the theoretical prerequisites, but also practical hints on how to work with your data in MS Azure.

Instructor Details

Anton is an assistant professor at the Higher School of Digital Culture at ITMO University. He received his bachelor's and master's degree in mathematics and Ph.D. in mathematical and theoretical physics from ITMO University in 2013, 2015 and 2019, respectively.

Specification: Introduction to Machine Learning


24 hours









10 reviews for Introduction to Machine Learning

3.5 out of 5
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  1. Vinayak Mehta

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

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  2. Gregory J Hamel ( Life Is Study)

    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.

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  3. Anonymous

    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.

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  4. Anonymous

    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!!

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  5. Hristo Vrigazov

    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.

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  6. Anonymous

    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.

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  7. Anonymous

    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.

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  8. Sergej Novik

    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.

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  9. Anonymous

    The best online course in introductory machine learning. The course is full of interesting quizzes. The instructor is very funny and interesting.

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  10. Anonymous

    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.

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