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미시건 대학교의 Applied Machine Learning in Python 학습자 리뷰 및 피드백

4.6
별점
8,251개의 평가

강좌 소개

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....

최상위 리뷰

FL

2017년 10월 13일

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!!

AS

2020년 11월 26일

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.

필터링 기준:

Applied Machine Learning in Python의 1,499개 리뷰 중 226~250

교육 기관: Michael D

2017년 7월 19일

I thought this was a fascinating course that tried to do the near impossible and succinctly summarise the key techniques of machine learning. And it did that very well. Very challenging tasks, but also overall inspiring for the next step.

교육 기관: DHANANJAY A 2

2021년 11월 23일

Excellent Course To Strengthen Your Skills In Machine Learning. Not Recommended For Beginners because It requires some amount of basic knowledge about python programming and also some basic terminologies knowledge of machine learning.

교육 기관: Vishesh G

2018년 9월 8일

This was an amazing course that I absolutely loved working on. It gave a deep insight into machine learning. I gained a lot of knowledge from this course. A must for the students who are just stepping in the field of Machine Learning.

교육 기관: Arturo B E G

2020년 5월 31일

It's a nice course, that accomplishes what it promised: overviewing ML algorithms from an applied perspective; however, I think that some other model selection methods (especially when comparing regressions) should have been included

교육 기관: Ganesh K

2018년 4월 14일

Tough and exhausting, but thoroughly worth it. I learnt a lot - and I already knew machine learning before taking this course. Be prepared to spend a lot of time preparing for the quizzes. The assignments are easier than the quizzes.

교육 기관: Manikant R

2020년 5월 9일

The course is well taught, by covering a lot of topics in short time, Yes you have to research a lot to get a full understanding, as the ML itself is not easy, you have to do hard work. I liked the references provided in the course.

교육 기관: Andrew

2019년 3월 11일

Really well explained theory without too much of a mathematical deep dive that provides a perfect set up to learn about machine learning from a purely math/stats perspective through Andrew Ng's Machine Learning course or self study

교육 기관: Lina J

2021년 1월 25일

That was a great and challenging course.

I am glad I took it, I learned a lot from the videos and especially from the labs. The last lab took me 5 days to figure out while I was benefit a lot from succeeding in coding it.

Thank you,

교육 기관: Michael L

2017년 6월 17일

Excellent high level advance course with in depth explanations. It is well structured. It learn me to applied Machine learning from very basics to optimum level. It help me to understand details of Machine Learning in Python.

교육 기관: anurag s

2017년 6월 29일

Clear, smooth and awesome course. Had fun learning the theoretical stuffs . Assignments and quizzes are really helpful in understanding the concepts. Last assignment helped a lot in applying the things learned in this course

교육 기관: Shreyas M T

2018년 9월 22일

Everything builds up very nicely on top of each other. A qualm some might have is that part of the assessments might be very simple. However, this is an applied course and the course material stays true to what it promises.

교육 기관: atul s

2020년 5월 21일

Making ML concepts accessible to the general public. If you are interested in gaining a basic understanding of the broad field, dive right in. Final assignment of Week 4 will really test all you have learned in the course.

교육 기관: Daniel N

2017년 7월 10일

I think this course is a real challenge and gives a great introduction to machine learning. I enjoyed it

thoroughly even if I had my troubles with the Quiz questions.. Great course overall, I would recommend it to anyone.

교육 기관: Mohamed H

2018년 6월 26일

C'est le meilleure cours en pratique que j'ai rencontré dans toute ma vie.je vous remercie énormément pour m'offrir cette cours et je remercié mon professeur pour la simplicité et la méthode avec laquelle a fait ce cours.

교육 기관: Dennis W

2020년 6월 7일

Absolutely must take for hands-on experience and practical knowledge. Instructor explained the tough course material in easy to grasp way. The assignments are challenging but achievable with time and reinforce learning.

교육 기관: Ashish C

2019년 11월 29일

This is the best course for machine learning. Assignments are really good. It make sure you know all the things that are taught to you. Even some times I had to go through the lectures again to complete the assignment.

교육 기관: MARKANTI B S

2020년 8월 14일

This is one of best machine learning course among I did . It about how to apply machine learning alogrithms rather than explaination how alogrithms works but a brief idea is given about that machine learning alogrithm

교육 기관: Pablo S C S

2019년 8월 25일

This course was a very very good introduction to ML focusing on SciKitLearn and using many real-life examples and datasets. Prof. Kevyn Thompson is very engaging and professional. I don't know how it could be better.

교육 기관: Abhay S

2021년 2월 4일

A great high-level overview course on machine learning. Great challenging assignments and highly conceptual. Putting everything together, building intuitions on different topics that one can leverage for lifetime.

교육 기관: Prateek D

2021년 10월 19일

Best course that I did on coursera. Learnt a great deal helped me a lot, thanks a lot University of Michigan and coursera as this course taught so many things which helped me to get placed in college placements.

교육 기관: Piotr K

2017년 11월 29일

Great course to gain basic ML skills and start building first models. Excellent starting point. Combined with Andrew Ng`s course on Machine Learning it`s great foundation for futher development as AI specialist.

교육 기관: Edwin V

2020년 6월 17일

Machine Learning Fundamentals are taught in concise and easy to understand manner. Some of the ML algorithms such as Kernelized SVM have been explained brilliantly. Thanks for putting up this wonderful course.

교육 기관: Limber

2017년 12월 3일

It is a very practical course if you have learned the Andrew Ng's Machine Learning course. It is much much more practical and I have gained a lot from it. I really wish I could learn it soon. Thanks very much.

교육 기관: Ayush D

2020년 5월 30일

Learned a lot from this course, very informative. One thing have to say that its not for absolute beginners, this course required prior knowledge of ml and python which will ease completion of course. Thanks!

교육 기관: Leonid I

2018년 10월 1일

Maybe this would be difficult to implement in a time-constrained course, but it would be nice to have more insight into inner workings of various algorithms... Because otherwise this course resembles botanics.