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Applied Machine Learning in Python に戻る

ミシガン大学(University of Michigan) による Applied Machine Learning in Python の受講者のレビューおよびフィードバック

4.6
6,766件の評価
1,219件のレビュー

コースについて

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....

人気のレビュー

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

フィルター:

Applied Machine Learning in Python: 26 - 50 / 1,199 レビュー

by Athira C

Jan 30, 2019

The course is so informative and interseting.

by Pawan M

May 04, 2020

This is an excellent course. If you will complete all exercises making sure you complete all questions in each exercise and score almost 100% in each quiz then you will get full value out of course. Deadlines can be reset any time so you can resume courses anytime and you can take your own time as per your schedule. The programming exercises can be solved only when you get the basics right. Else, you will need to revisit the course material.

by Haim S R

Jun 27, 2019

Gives practical experience with ML in Python.

Hides the math under the hood :(

However, this course is not enough to become a real data scientist. One needs much more exercises.

by Krishna B S

Mar 06, 2019

A very comprehensive and hands-on course for learning applied Machine Learning. Many thanks for this course.

by 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.

by 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.

by 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.

by 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.

by Mahmoud

Dec 28, 2018

Week three is the worst ..

Lecturer is getting confused a lot in an already confusing topic which ofc makes me resort to outside readings in order to grasp it and leading to stretching the time I need to finish this week

by Sajjad K

Jul 13, 2020

Teachers are very mediocre. They make way too many mistakes. Their pronunciation is stoic and muffled at times - makes it hard to follow.

by fulvio c

Feb 25, 2020

The video and training provided it's not providing enough information in order to complete the assignments.

by Rakesh D

Nov 11, 2019

lectures are boring, not updated but yes i learned something, but its not up to the margin

by Gregory B

Jun 14, 2017

I'm disappointed that I took this class, poor design and delivery. Machine Learning is an exciting and fun topic, but you'd never guess it from this class, and the way the instructor delivers the content. It's a shame that the designers want to throw every possible model at you in 1 or 2 weeks, before having a discussion on model evaluation. This course focuses more on the academic than the practical, and doesn't try to explain these topics in an approachable manner. There are far better and engaging options available.

by Saqibur R

May 03, 2020

This course is all over the place, and compared to the previous courses in this specialization, this seems like more of an effort to gloss over the documentation and capabilities of SciKit Learn rather than focusing on a handful of the most important ones. The course lacks focus, the material taught is not rich, and you are better off just reading the documentation on your own. The book recommended at the start of the course is excellent, and reading that instead might be more fruitful for you.

by Karim F

Jul 10, 2020

worst course of this specialization so far , the instructor is just reading stuff not making any effort whatsoever and it seems like he's obliged to do teach this course ,the autograder is the worst and the journey with this course is really painful i hope that you take these points in consideration and just delete this course

by Rishi R

Jul 06, 2018

Rather then writing code while explaining like the intro and plotting in python, the instructor shows it like slides, its hard to follow which chunk of jupyter notebook he is explaining, and requires lot of back and forth to read the code. Very bad way of explaining the codes.

by Sean D

Jun 12, 2019

This is the worst course in the specialization. The autograder is bad. There is inadequate explanation about when to use the different models. Presumes way too much about the student's level of knowledge. Would not recommend.

by Craig A B

Nov 02, 2018

There's too much back to back to back video lecture and not enough hands on work. The final quizzes and projects are too challenging given the amount of work done on the subject matter.

by Yuchen P

Oct 09, 2017

The materials of this course is poorly arranged: how is that even possible to cover gradient boosting, random forest, neural network, and unsupervise learning in a single week?

by Sudhir K D J

Feb 17, 2020

Very poor configurations. I am tired of submitting assignments on auto grader. This is the first time I am having such terrible experience with Coursera. Hope you improve.

by Marcos B G R

Nov 06, 2018

This is a really bad quality course. A little bit more professionalism would be advisable. I will continue to the next course and leave this behind.

by Rezoanoor/CS/Rezoanoor R

Mar 22, 2020

Faced problem in every assignment while reading the data sets. If the data is not in that folder what is the point of telling so?

by Omid

Sep 22, 2018

1- very slow paced lectures

2- very basic and elementary examples

To sum up, it is boring and not useful for practical application.

by Ipsita D

Apr 20, 2019

No visible support from groups forum. Videos knowledge is limited to complete assignment or quiz.

by Shaoqi C

Mar 10, 2020

This is my worst experience of submitting assignment and I found out that I'm not alone