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

4.6
stars
Оценки: 4,522
Рецензии: 780

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

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!!

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26–50 из 763 отзывов о курсе Applied Machine Learning in Python

автор: Guenael S

Jan 20, 2018

The class provides a perfect introduction to the scikit-learn Python module. The videos are engaging and insightful. The quizzes are challenging while not requiring too much time writing out solutions (it does take time finding some of the more subtle answers, by reviewing details in the videos). The executable modules are perfect to bootstrap machine learning projects. Homework assignments can get complicated, and you should be familiar with advanced data structure manipulation in pandas and numpy to make progress. Assignment grading is very well done.

автор: Anad K

Mar 28, 2018

Excellent course for Machine Leaning. Discusses wide range of Supervised machine learning and gives a very brief introduction on Clustering algorithms(Unsupervised). Users can immediately put to use the knowledge gained during the course.

Some more briefing about feature transformation and other such elements can be included in the course material to make it better. Also unsupervised machine learning could have been included with grater depth. Overall this course is highly recommended to aspirants interested in ML with some python knowledge.

автор: Matt R C

Feb 15, 2018

The course was very well prepared and the instructor presented the material clearly and informatively. I've seen some courses where you spend more time trying to understand and keep up with the instructor. In this instance, this was not the case and you could spend more time understanding the material. The instructor spoke slowly and clearly.

I do have to say I purchased the corresponding book as recommended but I didn't feel it was necessary. Good book, I just think the material in the course was presented well enough on its own.

автор: Ankur C

Nov 13, 2019

Great course for Machine Learning Algos. This series of lectures also helped me in understanding two beginners books for ML -

1. Introduction to Machine Learning

2. Hands on to Machine Learning.

Professor taught in a very informative and easy to understand way. Really thankful to the professor. Each and every algo is well explained with strengths, weaknesses.

questions in Quiz are very good these were not so easy and not so tough.

I will recommend this course if you want to learn ML using Python.

Thanks a lot, sir.

автор: Zhu L

Oct 23, 2017

The course is very well-designed, with the first three weeks learning basic know-hows of all the tools we need, and the fourth week make full use of every model we've learned.

Even people with no prior CS background can get along well enough.

Getting 100/100 out of the final problem is actually a passing grade, very easy if you use what you've learned so far the right way.

When you're willing to spend more time exploring the models, methods and parameters, the reward will be worth your efforts.

автор: Refik E

Sep 20, 2017

I thank Dr. Kevyn Collins-Thompson and Coursera team for the excellent course. I have learned valuable skills from the course. Dr. Thompson explained ML concepts very skillfully and made the course fun to follow. Assignments are very well selected and reinforce the class concepts. Over-all the course encourages learner to investigate and apply different ways to do same task. I recommend this course to those who are willing to learn machine learning and can't decide where to start.

автор: Krishna C P

Jul 04, 2017

excellent course for following reasons:

1. Excellent i python note books. What ever a student must know is kept in it.

2. every topic is explained simply and well upto what ever we need to know.

3. if you are not in academic field(not planning to do phd on this stuff). Trust me how ever advanced courses you do but after a week or month. these are the points which one need to remember.

4. Course and programming labs are in perfect sync.

Thank you very much for keeping this course

автор: SHAILESH K

Oct 21, 2019

Great intro course to Machine Learning. Gives you a good overview of the main models and Python needed to code. I liked the fact that it did not get too detailed into the Math foundations of ML. There are other courses for that.

I can apply what I have learnt right away on my job.

Highly recommend.

One Note: this course is over 2 years old and the Staff is pretty slow to respond. But the Forums have enough information to get you to self-solve your problem.

Good luck.

автор: Kedar J

Sep 25, 2018

Great course filled with a lot of details. The course does a great job in teaching all the important concepts. I felt the feature engineering should have been a dedicated topic. I got a lot of hints from the discussion forum and surprisingly there are even more concepts you have to learn for building a pipeline, treating categorical and numeric features differently. Overall challenging week4 assignment gives you confidence to deal with real world problem.

автор: Illia K

Dec 18, 2017

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.

автор: Ammar A M

Sep 02, 2018

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!

автор: Michael T B

Dec 19, 2018

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.

автор: Parvathy S

May 14, 2018

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!

автор: Benjamin S

Oct 27, 2017

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.

автор: Fabio C

Jun 22, 2017

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.

автор: Zhuohan X

Nov 04, 2019

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.

автор: Perry R

Jun 30, 2017

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.

автор: Fabiano R B

Mar 08, 2019

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.

автор: Ling G

Aug 18, 2017

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

автор: Alan J

Jul 02, 2017

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!

автор: Lawrence O

Jun 29, 2017

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.

автор: Peter B

Jul 11, 2018

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.

автор: Lingjun L

Jul 24, 2019

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!

автор: Shashi M

Sep 25, 2017

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.

автор: 吴昊辰

Dec 01, 2019

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.