Machine Learning Fundamentals

University of California, San Diego via edX

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Details

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Provider

edX
Pricing

Free Online Course (Audit)
Languages

English
Certificate

$350.00 Certificate Available
Duration & workload

10 weeks, 8-10 hours a week
Sessions

On-Demand
Level

Advanced
Subtitles

English

Found in

Part of

Data Science

Overview

Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?

In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.

Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.

Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.

All programming examples and assignments will be in Python, using Jupyter notebooks.

Taught by

Sanjoy Dasgupta

Reviews

3.9 rating, based on 8 Class Central reviews

4.1 rating at edX based on 14 ratings

Start your review of Machine Learning Fundamentals

Anonymous

This is the second course from UCSanDiegoX (early 2018 run) that breaks the enrollment clause stating "Audit this course for free and have complete access to all the course material, activities, tests, and forums". There was no final exam for Audit…

This is the second course from UCSanDiegoX (early 2018 run) that breaks the enrollment clause stating "Audit this course for free and have complete access to all the course material, activities, tests, and forums". There was no final exam for Audit students. Hopefully this will get corrected in the future runs of this course. The presence of staff on the Audit mode forum is very scarce, and the forums are divided into Verified/Audit students.

The course provides the "fundamentals" in only first few chapters. The more the courses advances the less mathematics is presented which hampers understanding of the subject. The quiz and homework problems have unlimited (!) number of attempts and provide no (!) explanations after the correct answer is given. This has been explained by the staff on the forum as a result of lack of time for preparing the course. If the missing explanations were provided, I would be giving the course 4 stars, and 5 stars if optional, more advanced mathematics videos were present.

The good things about the course: all materials were released upfront, which allows students for time management. There are many quiz and homework problems per week (order of 30) so there is an opportunity to practice, and the quality of jupyter notebooks is high, but both problems and notebooks are rather easy.

The instructor, despite shying away from math manages well to communicate intuitions about the methods used in machine learning, but Caltech's "Learning from data" https://work.caltech.edu/telecourse.html is still the best introduction to machine learning.
Anonymous

I have a few things to say about this course. Firstly, the lectures content is very good as the main concepts of ML models are explained well by Sanjoy Dasgupta. However, he misses on explaining the F1-score, precision and recall metrics which are…

I have a few things to say about this course. Firstly, the lectures content is very good as the main concepts of ML models are explained well by Sanjoy Dasgupta. However, he misses on explaining the F1-score, precision and recall metrics which are often used in solving practical problems.

Secondly, the course does not teach any python code which is fair enough. however, it asks to do the programming assignments in python and these are quite challenging with no solutions provided. One of them for week 7 even asks in a form of a quiz question 'Were you able to correctly implement the kernel Perceptron algorithm?' giving the options 'of yes', 'almost' and 'no, I had a lot of trouble with this'. Unfortunately if you've chosen the third option, there is no feedback to help you.

What makes it worse that the course staff do not monitor the discussion forum and those stuck with these assignments are unable to get help with their questions.

There is no way to contact the course staff either, as opposed to other courses on Edx which display the teaching university contact information on the course introduction page.

To sum up, this course is brilliant for auditing (i.e. watching the lecture videos) but becoming a verified learner is not worth it due to lack of staff support.
Luiz Cunha

I agree with others positive comments regarding this course. I have taken a few popular ML courses. This is probably the best one:
Instructor is top notch, good material, good videos and very clear explanations.
There are lots of exercises.
The only small objection is regarding the python notebooks and the related exercises: the notebooks quaility could have been better (wonder if the person that wrote them has any good CS experience); and the notebook question/exercises could have been better: either too easy, or requiring a touch too much unguided dev: would have enjoyed it more if it was more like in Andrew Ng notebooks in his popular Machine Learning MOOC.
Anonymous

I wouldn't waste time on this course. There are many books in machine learning.

Four of my favorites are:

_ Machine Learning for .NET Developers: Build Smarter Applications by Teaching Them to Learn from Data_ by Mathias Brandenwinder

_Machine Learning: An Algorithmic Perspective_ by Stephen Marsland

_MATLAB Machine Learning_ by Michael Paluszek

_Machine Learning Hands-on for Developers and Technical Professionals_ by Jason Bell

(I could expand this list with about forty more entries.)

Jim Collier

Orange County, CA

Christmas Day, 2020
Prakash L

Overall the course is great and the instructor is awesome. Machine learning is fascinating and I now feel like I have a good foundation. A few minor comments: some of the projects had too much helper code where the student only needed to fill in a portion of the algorithm. I would have preferred to have worked through more of the code. Also, there were a few times when the slides didn't contain the complete equations so it was difficult to piece it all together when writing the code. Lastly, I wish that there was more coverage on vectorized solutions for the algorithms.
ömer Yalçın

I think this course is one of most usefull machine learning courses at internet . Teacher is really good , there are enough math and code exercises
Pra

I audited this course. Professor Sanjoy Dasgupta you are a hero! Such a great teaching style. This is by far the best ML course in internet.
Anonymous

Fantastic course that teaches you the actual "fundamentals" of machine learning, one of the best machine learning courses on a MOOC. However there is a prerequisite that you must also complete the 2 courses (out of a set of 4 courses for the MicroMasters) prior first to get the best out of it!