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미시건 대학교의 Applied Machine Learning in Python 학습자 리뷰 및 피드백

4.6
별점
8,052개의 평가
1,472개의 리뷰

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

OA

2017년 9월 8일

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

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!!

필터링 기준:

Applied Machine Learning in Python의 1,463개 리뷰 중 126~150

교육 기관: Illia K

2017년 12월 18일

This course gave me some tools to use in real life. It's pretty abridged in time because they are trying to cover a very big topic in only 4 weeks. It won't give you a comprehensive set of knowleadges, but a good basis to proceed by yourself. Also some basic knowledges are reqired in computational mathematics, statistics and programming for applying this course. I highly recommend this course as a first step into machine learning.

교육 기관: Konstantinos V

2022년 7월 4일

A good start for someone who wants to learn machine learning in python, for example the commands etc. As for the overall machine learning in theory, I does not recommend this course because everything is explained very briefly. For me it was a great experience to finally learn to use python not only in machine learning but in data processing. I really enjoy the final assignment because I love exploring and processing the data.

교육 기관: Ammar A M

2018년 9월 2일

One of the best ML courses on the platform. I highly recommend it to all data-science enthusiasts. It would be nice to have pandas data-wrangling skills before tackling the final project as it is a must. Totally enjoyed the final project! was a great learning experience seeing my classifier AUC going from 57 all the way to more than 76 and the impact of feature importance and cleaning on the model performance was eye-opener!

교육 기관: Michael T B

2018년 12월 19일

Great class! I had fun learning many new things in this course. The professor did a very good job at taking a complex subject and making it simple and easy to understand. The code and assignments were straightforward and not overly difficult. The real quizzes/tests in this course were appreciated as this felt more like a "real class" where one can really learn a lot. One of the best online classes that I have taken.

교육 기관: Parvathy S

2018년 5월 13일

Very useful and true to the name, it teaches Applied Machine Learning - how and when to carry out the various algorithms on a dataset, how to tweak the parameters and tune the model. Really Really helpful if you're looking to finally get your hands dirty on data after reading all that theory!

Also gives brief but necessary summary to all the different algorithms with intro to deep learning as well. Highly recommended!

교육 기관: Benjamin S

2017년 10월 26일

I thought this was a very good course in Machine Learning using Python. I took Andrew Ng's Machine Learning course before this one, which I would highly recommend! I enjoyed this course because it taught me about scikit-learn, which I plan to use in my career. I also purchased the recommended textbook "Introduction to Machine Learning with Python" from O'Reilly, which I found to be a very useful reference.

교육 기관: Fabio C

2017년 6월 22일

The course is well done and both the lectures and the practical assignments have generally a high quality. If you come from a theoretical background, be aware that this is a very "high level" course, meaning that a lot of attention is put on the practical application of the different ML methods (using the sci-kit learn library in python), but very little is said about their mathematical foundations.

교육 기관: Zhuohan X

2019년 11월 4일

All complicated math acknowledges were cut off and fully focused on applying ML using python. As an energy engineering master student who doesn't have much programming experience, I find this course very useful. PS. I've previously taken the specialization 'Python for Everybody' to get familiar with python. I suggest doing the same if you also have no idea of python just like I did when I started.

교육 기관: Perry R

2017년 6월 30일

Excellent instruction and challenging assignments! Sophie from the teaching staff was very helpful and responsive to forum posts. Thanks to Kevyn Collins-Thompson for a great survey course in machine learning. The only downside was that the auto grader has limitations which inhibited some exploration (one can not keep plots in the submission is an example), but I'm sure that will get worked out.

교육 기관: Fabiano B

2019년 3월 8일

The course is a great overview of the basic algorithms that every machine learning practitioner should know. Since it has a limited amount weeks to cover such a broad subject, you will have to dig a little deeper by yourself. I found the reading material also very interesting. The final project is awesome and it will definitely make you experiment what is exactly what a Data Scientist should do.

교육 기관: Ling G

2017년 8월 17일

This is a great course I learned a lot, especially it familiarize me with the SKlearn toolkit which is very very handy. I notice that the SKlearn documentation contains a good figure which shows a rule of thumb which learner to use. I recommend you to include in course reading, because some students might find it very useful.

http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html

교육 기관: Nattamon T

2022년 6월 21일

The course does a job on giving an overview of how each machine learning algorithm works without having to go deep into details. Instead of mathematical destials, the course places emaphasis on how machine learning is applied to solve problems and some practical consideration. I took machine learning courses during my master but I found this course a great complement on what I have learned.

교육 기관: Alan J

2017년 7월 2일

This was an awesome and engaging course. Machine Learning is a vast field with lots of ground to cover. This course gives a broad overview of all the different parts of machine learning without going too deep and also keeping everyone engaged. The assignments, especially the last one test what you learned and keeps you on your toes. A good beginner course to Machine Learning. Thank You!

교육 기관: Lawrence O

2017년 6월 29일

Very informative about machine learning approaches ie supervised and unsupervised learning. And then goes into detail about the techniques such as regression and classification for supervised learning and clustering (K-Means) for unsupervised learning. Other techniques are discussed such as Principal Component Analysis etc.

I enjoyed it and would recommend for all data enthusiast.

교육 기관: Peter B

2018년 7월 11일

Kevyn is an absolute joy to learn from. His enthusiasm for the topic is contagious, and his explanations are clear. The course content is well curated, tested, and reinforced. At the end of this course I feel confident that I can *actually* apply machine learning to real world problems and competitions. This is not just a 'good' course, it's a new gold standard in e-learning.

교육 기관: Irene Z

2019년 7월 24일

Much more detailed than the previous two courses. The lecturer teaches with more verbose slides and thus gives you a more detailed overview than the lecturer in the first two courses in this specialisation. The assignments are much easier as well. But still thoroughly useful and I have to admit a welcome break from the gruelling process that typified the first two courses!

교육 기관: Luigi C

2021년 4월 24일

The course allows you to play with a multitude of supervised (and unsupervised - optional) machine learning methods. Professor Collins-Thompson is very clear and knows perfectly well how to convey concepts and how they should be applied in real situations. I recommend it to anyone like me who needs to be able to develop simple codes and understand what they are doing.

교육 기관: Shashi M

2017년 9월 25일

Very good course for a wide spectrum of audience interested in Machine Learning. I just had a basic learning of ML and Python, but the course was structured so well that I could catch-up. Also offers an interesting peak into Neural Networks and Deep learning. Overall, an excellent course with clear and attainable objectives, backed by high quality content and data.

교육 기관: Haochen W

2019년 12월 1일

This is great course with very practical methods to sovle real problems in various fields. I think there should be a additional course regarding Deep learning, which I think would be very successful as well.

Moreover, this course can be combined with Andrew`s ML so that we can have both theoritical concepts and practical experience of Machine Learning in python.

교육 기관: T.V.S T

2020년 9월 24일

This course gives you a very good knowledge how to apply machine learning techniques (mostly supervised learning) and basic things, like how to preprocess the data and what are the pros and cons of various models and which models to be used based on the kind of data given, and many more basics which are required for a deeper understanding of Machine Learning

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교육 기관: xixicy

2018년 4월 10일

The content (slides, python scripts) is very structured. The lecturer explained very clearly. The reference articles were super inspiring. Also, the assignment is very well designed and relevant to what's covered (in comparison, some other courses might have very difficult assignments which need much more self-learning and cause frustration). Thank you!!

교육 기관: Ben L

2018년 3월 14일

Excellent course, easy to understand, useful and enjoyable to do! Two minor comments: it took me a longer than the estimated times to complete the Quizzes; I have Python programming proficiency and a small amount of background in Machine Learning. I would have preferred the final assessment to have an extension to it which required a more advanced model.

교육 기관: Fabrice L

2017년 6월 24일

Great course!! And this field of science/technology is fascinating.

The only comment that I would do is that it might have been useful to include a whole pipeline on the creation of a simple machine learning software from the data collection to the end result. I guess that is the goal of the next course on text processing, so I'm looking forward to it.

교육 기관: David V

2017년 7월 28일

Excellent course!

Machine Learning is today a buzzword and you do not really know what it is until you do it. The University of Michigan has put together a great program that takes you from the basics of Python to the latest Machine Learning techniques.

I started without knowing Python, and well, I cannot say that it has always been easy, but I DID IT!

교육 기관: Alexander T

2019년 6월 1일

Thank you all for such an awesome series of courses.

I find these courses really challenging, especially the final assignment. But it is rewarding too, coz you feel, that you CAN solve such tasks in real life too.

Thank you Michigan team for such efforts. During the last 1.5 years I managed to progress from 0 programming knowledge to solving ML tasks