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Отзывы учащихся о курсе Applied Machine Learning in Python от партнера Мичиганский университет

4.6
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Оценки: 4,566
Рецензии: 787

О курсе

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python....

Лучшие рецензии

FL

Oct 14, 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!

OA

Sep 09, 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

Фильтр по:

726–750 из 769 отзывов о курсе Applied Machine Learning in Python

автор: Matteo B

Aug 10, 2019

Assignments are not really supported by the material provided (videos). The level is not balanced. Some bugs in the assignment code as well

автор: Halil K

Sep 27, 2019

Good content, bad teachng staff. Though the discussion forum contributors were very helpful and should be commended for their efforts.

автор: Ankur P

Mar 30, 2019

Unsupervised learning was missing. The codes written in the lectures were not explained clearly. Some topics looked unimportant.

автор: James F

Feb 13, 2018

Good overview of methods. A bit too intense at times though, may have been better to really focus on a couple of key concepts.

автор: Darshan S

Dec 31, 2019

Not enough real life examples throughout the video, makes it very hard to concentrate during the whole lecture.

автор: Mauricio A E G M

Nov 17, 2019

This course is not useful to learn from scratch, but has some good things, for example the final assignment.

автор: Nikola G

Jan 14, 2019

Really didn't like the quiz parts of the course. If it was up to me I would do thorough revision of these.

автор: Gilad A

Jun 27, 2017

The last assignment was super. apart for it, the assignments and the course were too easy

автор: Philip L

Oct 31, 2017

The assignments are extremely difficult, professor is a bit dry during lectures.

автор: Pakin S

Jan 10, 2020

How can i pass without reading discuss about problem with notebook

автор: Hao W

Aug 27, 2017

The homework is too easy to improve our understanding of ML

автор: Navoneel C

Nov 21, 2017

Nice and Informative but not practically effective

автор: Sameed K

Mar 15, 2018

have to figure out a lot of things on you own.

автор: Andy S

Jun 04, 2019

It could have been better with more examples.

автор: Jeremy D

Jul 10, 2017

The topics were good, but too many were d

автор: Ryan S

Dec 12, 2017

Homeworks are inconvenient to submit

автор: Wojciech G

Oct 28, 2017

To fast paced.

автор: Milos P

Jun 27, 2018

Decent material and I appreciate the amount of hard work that went into building the course. However, the course should really be titled "Evaluating Classification Methods", as that is pretty much the focus of the entire class. The lectures (especially in Week 2) were SOOOOOO long and very hard to absorb, that even double-speed didn't help. In education, less is more. I would compare this course to the reading of a textbook. There was very little focus on making sense of the code and solving real-world problems and far too much emphasis on shotgunning (what felt like) every single classification technique known to man and trivializing pros and cons of each method. To make matters even more strange, PCA and other useful methods were pushed into "optional". This course should really be a two-part course, especially since the claim is that the course requires 18 hours of time. Sure, type in the code just as the professor does and you get the right answer, but meaning is lost of if you are to adhere to the timeline. If I didn't know more about machine learning and this class had been the first one I had taken, I couldn't run fast enough from the pursuit of a career in this field. Data analysis is intriguing and the methods are varied and fascinating. For me personally, this class was a let-down. Again, I recognize the course was hard work; I am merely stating my personal sentiments.

автор: Taylan T

Oct 20, 2019

TLDR; Boring and unstructured courses that do not offer insight. You learn by doing the assignments.

The video lectures are boring and unstructured. You can tell the lecturer really hates what he is doing often sulking and showing zero enthusiasm. Also, he makes you question if he really knows what he is talking about. I am sure he does but his attitude and sloppy mistakes give you doubt. The format of the video lectures is that the lecturer reads a script in front of the camera and the algorithm he talks about is shown in cutscenes. This is a terrible idea. Also, the courses are not well prepared, lacking continuity. On top of that lecturer often makes mistakes and these mistakes are "corrected" by showing you a cutscene that writes the professor wanted to say X instead of Y. This is really sloppy. This is not an open course where you put your recorded lectures to youtube for free. You are delivering these lectures to paying customers. Seriously many free lectures on youtube are better built compared to these lectures. I have learned a lot of things in these lectures by doing the assignments and trying to learn by using google and not via lectures. One positive thing about this course is that there are some good links to papers, websites etc... But you need a lot of time to go through them.

автор: Shiomar S C

Oct 14, 2019

Honestly this course was somehow disappointed I really wanted to learn a lot but the professor was somehow discouraging, he repeated himself a lot, and for an online course and every video been 20+ minutes long and at the end only been useful 4 or 5 min of it… having so much errors during lecture and not following the notebook as it was given to us make it more difficult to learn… I’m choosing this platform (and paying) due the professor been good and this one make learning more difficult than the previous one.

автор: Justin F

Sep 27, 2017

The quality of this course in the series is a far cry from that of module 1 and 2, which is a shame because this is the one that I was really looking forward to. The professor does not seem comfortable and uses a lot of extra words in his lectures which can make them confusing and rambling. Many questions on the quizzes and assignments are not covered or well explained by the material. Many assignment questions have to be explained by teaching staff on the forums because the task is not clear.

автор: Josh J

Jul 09, 2018

Although the course taught me a lot on the importance of parameter tuning and data leakage, I found that often times it was too technical and did not provide the information I was looking for. I found myself continuously referring to notes from other ML courses during the length of this course. In addition, the video errors and challenges with the auto grader were very frustrating.

автор: Gregory O

Sep 25, 2017

I was excited going into this course because the others in the series were taught well and I had learned a lot. Unfortunately, this course greatly disappointed. The lectures were dull, included a lot of mistakes, and did not cover most of what was expected during the assignments. All in all, this course was a waste of time versus just learning scikit-learn on your own.

автор: Olubisi A

Jan 11, 2019

I think this course would be a bit challenging to someone who is new to machine learning. The professor often glosses over import details and moves a bit quickly through the course material. There needs to be more powerpoint and reading material explain what the videos explain.

автор: Amir A C

Jan 19, 2020

Unfortunately, for me, this course (not the specialization) seems to be a "review of" Applied Machine Learning in Python" rather than "teaching" Applied Machine Learning in Python. Some codes used in the notebook were skipped by the instructor.