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Opiniones y comentarios de aprendices correspondientes a Introduction to Machine Learning: Supervised Learning por parte de Universidad de Colorado en Boulder

22 calificaciones

Acerca del Curso

In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. In this course, you will need to have a solid foundation in Python or sufficient previous experience coding with other programming languages to pick up Python quickly. We will be learning how to use data science libraries like NumPy, pandas, matplotlib, statsmodels, and sklearn. The course is designed for programmers beginning to work with those libraries. Prior experience with those libraries would be helpful but not necessary. College-level math skills, including Calculus and Linear Algebra, are required. Our hope for this course is that the math will be understandable but not intimidating. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at

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1 - 13 de 13 revisiones para Introduction to Machine Learning: Supervised Learning

por Alessandro U

30 de ago. de 2022

Most probably the reason is simple: the course is not for beginners, and I am a beginner. That said, through the course I regularly found myself facing programming assignments I was not prepared enough to do from the lessons in the course. I found irrealistic that after 10 min of video one can actually write the functions of a machine learning algorithm he knows from 10 min. Sometimes the answer to parts of the programming assignments were barely (if not at all) mentioned in the videos. Of course I managed to find solutions and I ultimately finished the course (although right now I am currently waiting for what I think is a bug, to be fixed). But it took me months. I remained stuck onto problems for weeks with hints from the autograder as vague as "did you check that the function works?". Thinking back to when I started the course, surely too much naively, I unfortunately cannot say it matched my expectations.

por Pratik P

19 de jul. de 2022

The course is misleading, the python part is completely neglected and the assignments is not properly decribed to be able to perform. The theory can be found in any statistics courses and books. Implementation is a huge issue to most.

por Vishal P

16 de jun. de 2022

I would not recemmend this course. I was looking for a course where the instructor to teach concepts and provides examples. The course is designed around on reading and the lecture does a quick overview of what is read and doesn't do justice.

por Zehu C

4 de abr. de 2022

the course is comprehensive and rigorous and provides good exercise with the assignment. But the lecture is not clear enough with a new concept and didn't really provide a good example explaining them. And the auto-grade assignment is difficult to finish because the instruction is not clear and the lecture didn't provide much on how to do the assignment.

por Miguel D B

1 de sep. de 2022

I think this course provides a fair balance of videos, readings and exercises. The course provides 2 free books (one is basic and the other requires more math), which the reader can follow along with the videos. I think this is the right approach, because learning from books is a desirable meta skill. Knowledge of Python programming and very basic statistics are required.

Also, for more advanced topics, one can always find lectures from other top institutions in youtube.

I had a good time learning from this course. Thank you!

por Nathan H

5 de abr. de 2022

The auto-graded assignments in this course offer much better feedback than some of the other CU Boulder MS-DS courses that I've taken but they still have issues with confusing, incomplete, or incorrect instructions and cryptic feedback.

There's a lot of good material in the course. The coverage seems pretty basic, but that's fine. The last section (i.e. week) which deals with support vector machines doesn't hold together as well as the rest of the course.

The course contains peer graded assignments which are fine in principle, but it seems like Coursera will only let me do the required "grading" part of them when the deadline gets close. That interacts poorly with the due date resets and means that the course isn't really self-paced. I also received a non-passing grade on a module three hours before the due date closed it off when I had submitted it a month before.

por Ashish R

21 de jun. de 2022

This is one of the worst ML courses out there. So many mistakes and virtually inactive discussion forum. DO NOT TAKE THIS COURSE !

por Francesco M

4 de nov. de 2022

This course is not for beginner. As wrote in description, the course is aimed for people with already know about probability calculus and statistic inference. Thus, is an intermediate level course, clearly.

Beyond all this, the student is called to study on book of the course and don't rely only on lecture videos. The course is good and good are also the practical tests.

I am felling to advice this course.

por Mahmudul H

21 de may. de 2022

This was an excellent introductory course that allowed me to get into the world of Data Science and Machine Learning.

por Js S

24 de ago. de 2022

Most of the assignments are challenging and invite you to implement the ML algorithm looking under the hood. I specially enjoyed the PCA assignment; it helped me understand how eigenvalue decomposition is used to calculate the principal components. I also enjoyed reading the ESL. That book is a fundamental source in ML. I think there is room for improving the slides showed in the videos. I also recomend to review the topics asked in some quizes. I think somes topics are not covered in the readings and videos.

por Donald F

20 de nov. de 2022

I thought this was a good introduction to machine learning. It is light on the theory and mathematical side, but focuses on the practical aspects of programming ML algorithms using Python. I had taken a university course for my masters in statistics that covered the material in "An Introduction to Statistical Learning", but we used R for programming rather than Python. I came into this class with the theoretical underpinning, but not much experience in Python - the class helped to close that gap.

por Mario A h C

14 de may. de 2022

I'm not sure why it did not click for me.

Perhaps too independent for me. It would be great if the videos share more code and how to use the tools and resources offered.


por Kenneth W

27 de dic. de 2022

The course could be far better than it is. Videos cover the overall concepts but are completely lacking in the Python-related information that is needed to do the examples. I am a pretty good programmer, but not an expert in Python. I found that the programming assessments use some unusual approaches in them to reflect the overall concepts in the videos.

This course needs a better balance between concept and code.

I can easily set up a test scenario using sklearn by pulling in a set of data and splitting it into a test and training set, then fit it to assess the performance of the Model using those test and train sets, but there is no time spent on showing how to do that properly in python in this Introductory course.

More practical real world useful python examples need to be covered in the videos otherwise the student is left to scouring the internet for the information they need. Many times I find that to be large waste of my time and find little to no good (or wrong) examples of how to use python for machine learning. It would best if this course focused on teaching conecpts and a decent reall world approach tha one could use as a basis for later classes, but it fails at doing that.