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.
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Machine Learning Fundamentals
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- Provider edX
- Subject Machine Learning
- Cost Free Online Course (Audit)
- Session In progress
- Language English
- Start Date
- Duration 10 weeks long
- Learn more about MOOCs
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Overview
Taught by
Sanjoy Dasgupta
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for edX's Machine Learning Fundamentals
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Anonymous
a year ago
completed this course.
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 ham…
The course provides the "fundamentals" in only first few chapters. The more the courses advances the less mathematics is presented which ham…
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.
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.
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ömer Y
4 months ago
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
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