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Вернуться к Applied Machine Learning in Python

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

4.6
звезд
Оценки: 7,161
Рецензии: 1,299

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

AS
26 нояб. 2020 г.

great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.

FL
13 окт. 2017 г.

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!!

Фильтр по:

1176–1200 из 1,277 отзывов о курсе Applied Machine Learning in Python

автор: Shashi K

18 мая 2020 г.

very good learning

автор: hamzaoui m

25 июля 2019 г.

HARD BUT GOOD

автор: Mrs K S

2 окт. 2020 г.

nice course

автор: Aditya V

3 июля 2018 г.

Excellent!!

автор: ISHAN H S

23 июля 2017 г.

Awesome !!!

автор: KILLANI T

10 июня 2020 г.

hard a bit

автор: Deepak

13 янв. 2020 г.

Very Good

автор: Md J A

18 авг. 2017 г.

very good

автор: MOHD A

10 сент. 2020 г.

perfect

автор: tanmoy p

18 дек. 2020 г.

good

автор: sushant s

28 нояб. 2020 г.

Good

автор: Anant K

26 сент. 2020 г.

GOOD

автор: Sajal P

12 авг. 2020 г.

....

автор: Latha B N

9 июля 2020 г.

Good

автор: Yzeed A

30 окт. 2019 г.

Good

автор: Ketan S R

4 июля 2019 г.

.

автор: Navish A

20 июля 2020 г.

I just completed the third course (Applied Machine Learning course) over the last 7 days.

Good:

The course syllabus is quite well designed for an applied intro ML course

Assignments are nice & force you to think; you cannot simply watch the lectures & complete them straightaway; which is good in my opinion.

Needs to Improve:

The lectures are atrociously boring. The professor seems to be reading out from a teleprompter in a flat pitch.

There are parts where the intuition behind the concepts are well explained and others where you are left staring at stars and better off learning from other sources over the net.

The course seems to have been all but abandoned. Common mistakes in the assignment setup & lecture recordings have not been corrected since the course was first offered 2.5 years ago. The discussion forums keep getting spammed on similarly asked questions which can be easily solved by correcting the assignment errors and providing a few clearer comments/instructions. Week 3 lectures definitely need to be re-recorded as there is a correction prompt on every video. There is one 'Mentor' who helps out as a volunteer. No one else to moderate the forums.

The course pace is quite uneven and patchy. Week 2 is extremely heavy while week 1 super light. Week 3 is good but week 4 feels half done/rushed. Seems like there is an arbitrary administrative requirement to do a four week course from UMich.

All in all, I did not come away impressed & elated from the course. I did expect much better from my Alma Mater.

автор: Nigel S

9 июня 2019 г.

This is an OK introduction to Machine Learning. It covers a range of relevant topics. The gap between the lecture content and the assignments is the typical chasm for this U.Michigan "speciality", and frankly you end up basing assignment answers more on internet research rather than lecture content.

I'd sum it up as a substantial missed opportunity. The last assignment is really good in terms of doing a realistic Machine Learning project, but the preceding course content doesn't give you the tools or frameworks to do that project in a logical, industry standard workflow. It gives you an idea of what the tools are, but not how to really apply them all together in an efficient and logical series of steps.

It's as if those who designed the course decided that learners needed a tough-love approach, like a trainer lying down on the grass and showing learners swimming strokes, and then just throwing those learners into a pool and expecting them to keep afloat, and combine what they remember with what they see other more experienced swimmers in the pool doing. It shows a fundamental misundestanding of the Coursera learners usually being very time poor and expecting much more from the instructors.

автор: Jonathan B

21 окт. 2017 г.

This course provided a good structure and order to learn introductory machine learning concepts in Python. However, I thought the lectures in particular were needlessly more abstract than the previous data science courses in this specialization.

In my experience, learning a new programming concept comes from practically writing code then observing what happened. The earlier data science courses were great because you could test code with the lecturer as the video progressed and learn from it.

The lecture content here structured to discuss broader machine learning concepts, rather than setup to follow along in the notebook. I found this was okay for introducing the idea of different machine learning concepts, though without the practical application and observation it became difficult to remember these concepts or test what I was hearing. I found most of my learning happened in the assignments or by following more practical online resources. The course could be improved by tying the notebook modules more closely to the video content, making it easier for learners to follow along.

автор: Ryan D

15 июля 2019 г.

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.

автор: Jennifer W

11 нояб. 2020 г.

I felt like each standalone topic was explained okay, but I didn't get a good big picture understanding at the end. There wasn't a good wrap up to explain holistically how to choose one classification method over another.

There are also just too many mistakes in the lecturer speaking as well as in the slide. For an online class I would expect that Coursera would redo the video or at least the slides as they interfere with learning.

I felt that the lecturer belabored easy points, like calculating precision and recall by hand but then didn't explain other topics regarding the classification methods well. I did not find the graphic visualizations in his slides helpful in explaining hyperbolic tangent functions.

автор: Dimos G

3 сент. 2019 г.

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.

автор: David M

19 окт. 2018 г.

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.

автор: Gu X

19 окт. 2017 г.

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.

автор: Pablo S

21 авг. 2020 г.

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.