Chevron Left
返回到 Applied Machine Learning in Python

學生對 密歇根大学 提供的 Applied Machine Learning in Python 的評價和反饋

4.6
6,556 個評分
1,172 條評論

課程概述

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....

熱門審閱

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

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!!

篩選依據:

1076 - Applied Machine Learning in Python 的 1100 個評論(共 1,155 個)

創建者 Ryan D

Jul 15, 2019

I'm glad there was an introductory course like this offered for machine learning. The content is very accessible and the assignments are simple enough to work through without frustration, but challenging enough to help you understand how to apply machine learning algorithms on your own.

I did purchase the book recommended, Introduction to Machine Learning with Python by Andreas C. Muller and Sarah Guido. The lectures in this course are basically paraphrase the book. Frankly, I think you'd get more value from this course if you read Chapter 2 in its entirety and follow along with the juypter notebooks provide with the book. It's easy to tell when someone is teaching you vs. reading to you— this course's lectures were definitely the latter.

創建者 Dimos G

Sep 03, 2019

This course was a complete disappointment. First of all, it should have been split into two courses. The second week especially contains so much material to the point that it's not-pedagogical. Also, I regret to say that the instructor is not fit for this task. It would be better if they used Christopher Brooks from the first two courses as he is more engaging and he seems to have a lot more experience in public talking. Another thing is that there are serious bugs with the assignments. This course needs serious redesign.

All in all, don't spend your precious time and money on this one. There are better courses available on this subject.

創建者 David M

Oct 19, 2018

The quality of the teaching is a marked improvement over module 1 & 2 in this specialisation. In my opinion it would be a 4/5 star course on that alone however there is 1 minor and 1 major issue. Starting small, the course could do with better summary notes/cheatsheets to help remember details and as prompts when doing assignments; I found it really annoying to have to skim read the lecture video transcript or scan through the videos. The MAJOR issue is the problem sets and the autograder. I really feel the teachers need to re-write this whole section before I could recommend this course.

創建者 Gu X

Oct 20, 2017

Most of the content professor taught are intuitive, but the PPT seems helpless. Furthermore, the thinks in the course are shallow depth, conversely the assignment are little bit difficult especially on assignment4. I mean if the goal is to train our to do some real world data you may can shrink the dataset, the large dataset would takes more time to training which would cost more time to debug. Anyway, this is a great course but I think it's better to do slight change on the quiz and assignment.

創建者 Pablo S

Aug 21, 2020

This is a good introduction to applied machine learning with python. Although it is "applied" it would be worth to cover the basics of the presented algorithms a bit more thoroughly. In paticular, I think that the regularizations parameters and their role in bias and variance are not presented in a very clear way. On a different topic I think that the course deserves to be updated with latest sklearn implementations and correct a lot of bugs in the assignements and lectures.

創建者 Melanie B

Jul 18, 2017

This course helped me to get started on using Python for machine learning tasks.

Personally I would have preferred a more mathematical approach when discussing the various machine learning techniques, in order to learn more about what's going on "under the hood" in scikit-learn. I know that the course is called "Applied Machine Learning in Python", but to me it felt more like "Extremely Applied Machine Learning in Python" :-) Other than that, I enjoyed this course!

創建者 Carl W S

Jul 02, 2017

There is a lot of good material in this course, but it is noticeably not taught as well as the previous two courses in this specialization. The lesson plan feels like a class lecture modified just barely enough to work as a MOOC, the autograders are highly finicky, and most of the programming assignments had errors or missing details that required the learner to check the class forums to find out how to fix them. Overall, it was a helpful course, but felt unpolished.

創建者 Daniel K

May 24, 2020

I do think the lectures are very well done and I believe I learned a lot. However the programming assignments part was frustrating, there are a lot of issues with the autograder, loading files etc. I would appreciate if the steps were described in greater detail. Some parts are very easy, just blending together a few pieces of code from the lecture, and others very difficult, built on things not covered in the lectures. The last assignment was the perfect balance.

創建者 Thiti C

May 31, 2020

This course is, in fact, excellent. One can learn a number of algorithms used in a machine learning practically. This course does not focus much on mathematics behind tools we used, the professor taught a lot about the practical one. However, some of the parst in this course are too rush; you have to understand a lot of concepts in Python berfore entering this course, including basic Python syntaxes, and practical libraries such as Numpy and Pandas.

創建者 Adithyan U

Jul 03, 2019

The course tries to do too much in four weeks. Consequently, the teaching material isn't as comprehensive as it ought to be. I've probably spent over 10-15 hours cumulatively on other websites, trying to comprehend the intuition behind the algorithms used. This course isn't great at getting that across. There's a lot in here that we're forced to take for granted. I'm afraid I'll have to think twice before I choose other UMich courses in the future.

創建者 Charles L

Mar 18, 2020

The material seemed ok. Really annoying that this course genuinely had incorrect code in the homework assignments. It seems that some documents changed directory and were different in the homework folders, vs the grading tool. resulting in failed grades where tests worked just fine. Easily fixed, but why would I have to? Really hurts the notoriety and reputation of this program to have such simple frustrating errors. (on 3 of 4 assignments!)

創建者 Amit S

Apr 14, 2019

It would be better if this course was not with Jupyter notebooks. Professional data science projects will not use notebooks but script files instead. The course should prepare students for professional projects by using script files.

Also the lecturing is very rigid and scripted which makes it less engaging. There is also no material on how any of the algorithms work in detail however there is good material on scikit-learn.

創建者 Koo H S

Mar 08, 2020

While the course material is very helpful and reasonably pace, I felt like I'm always battling the autograder to pass the assignment. I do think that I spend more time to get my answer accepted by the autograder than actually working on the assignment itself. I think an easy way to fix this is to clearly layout the tips to get pass the autograder, rather than having the students to search through the forum for a solution.

創建者 Joseph D P

Nov 15, 2017

I feel like the assignments for this class were very lacking compared to the other courses in this specialization. They were glorified code copy and pasting and didn't make you learn much. There was much more video instruction than in the other courses in this specialization, though. Definitely would recommend reading the accompanying O'Reily book to help you understand the difficult concepts better.

創建者 Eric M

Jun 30, 2017

I learned a lot from this course, but I do not feel like I truly understand everything. There was an extraordinary amount of information that made it difficult to keep on track and take everything in, not to mention apply the concepts in the assignments. I feel confident with the concepts and I could do much better in the future with more practice with skills developed from this course.

創建者 Anand M

Jun 22, 2020

The course is good but I expected a faster response for my regarding assignments & course materials .

Can you guys make sure that your mentors reply faster to student queries ?

You need to make assignments more descriptive as lot of time is being spent on forums to just understand the problem correctly.

The autograder behaves erratically lot of times so you need to make it more efficient.

創建者 Sourav P

Oct 27, 2018

Nothing wrong really. Should have provided more mathematical theory in the resources section.

Assignments should be a lot tougher and on real life data sets which require recodings and transformations. Quizzes should be more relevant to the lessons taught. More hardcore theoretical resources, like books and research papers should be included in order to complement the practical lessons.

創建者 Muhammad H R

Jan 19, 2018

This course was too theoretical and lacked any practical exercises that would help me solve any problems. The professor went too deep into the concept and in the end you were left wondering what is the purpose of the algorithm. Seems as if they were concerned in covering a specific amount of topics rather than making the concept of machine learning more approachable.

創建者 Fatemeh M

Nov 25, 2018

Hi

First I want to thank all the instructors and anybody that was involved in this course preparation. That was a great opportunity and I really liked that but not in all parts . I think the syllabus was a little heavy and somehow I couldn't follow that . in the programming part I needed more guide and sample .

But in general It was good and I thank you so much.

創建者 Mario P

Dec 08, 2019

I struggled with this course. The lectures cover a great deal of information extremely fast. I appreciate that there are more lectures than in previous courses in the specialization and the information is better presented IMHO. The assignments were quite difficult and I struggled. Relying heavily on discussion forums and online posts.

創建者 Vatsal K

May 24, 2020

I think the instructor must give more practical explanation for scikit-learn. I need to research almost everything for completing a particular assignment. Please have changes in pitch of your voice while delivering the lectures so the lectures don't seem boring. Also, please update the autograder !

Overall a good course. Thank you.

創建者 MD T R J

Apr 12, 2020

The course material is good, but the teaching style is too boring. Without the standstill slides, if there is animation, it would be helpful for us. And, the assignments are not straight-forward and the autograder is buggy. As an example, I can run the assignments easily in the jupyter, but the autograder faces problems.

創建者 Jun L

Nov 07, 2019

There are too many errors in the video and even in the quizzes and assignments which will affect the final grade and wastes studying time to figure out it is an error. It is pointed out in the discussion forums but no one is taking the action to correct it. Moreover, at least 3 of the reading materials fail to be loaded.

創建者 Ishan D

Sep 20, 2020

Good course for beginners. However, things like feature selection, dealing with null values, model selection should be in depth and an end to end example on a real world dataset should be explained step by step to with best practices to develop learner's interest towards picking up problems and solving on their own.

創建者 devansh v

Jul 17, 2020

Course is good but leaves a lot of things unexplained and feels like the weeks explaining ml algorithms are in a rush.But the assignments are truly remarkable.I would recommend this course to anyone who already knows machine learning and would want to apply it on some good problems/assignments before Kaggle.