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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: 7,244
Рецензии: 1,318

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:

1026–1050 из 1,295 отзывов о курсе Applied Machine Learning in Python

автор: Amit P

26 дек. 2019 г.

This course is an excellent run through of the pipeline for developing, running and evaluating machine learning models. The video lectures were monotonous and long, though. The last assignment was especially meaningful and enjoyable. Highly recommended.

автор: Donald V

17 дек. 2017 г.

If I could I would give this course 3.5 stars. Most of the coverage of the concepts in this course were pretty light and there were several issues with the autograder being difficult that made this course a lot less enjoyable than it could have been.

автор: tanuj

8 сент. 2020 г.

There were a few mistakes in the assignments which causes unnecessary time wastage on student's end. Otherwise, it was quite a good course.

Also including a demonstration of encoding textual data while implementing Random Forest would be helpful.

автор: Cole M

30 авг. 2020 г.

Good practice content and good explanations. Some of the content I would rate as great. There could have been more smaller programming exercises that built up to the main exercise for each week. This is the only reason I did not rate as 5 stars

автор: Alex W

18 нояб. 2019 г.

Lots of minor issues with the Jupyter notebooks that could easily be fixed but the instructors just post a way to solve the problems in the discussion form instead which is frustrating. The material itself was extremely interesting and useful!

автор: Siddharth S

11 июня 2018 г.

It would have been wonderful if the notebook codes were written and explained in the video the same way as in earlier courses in specialisation taking care of the implementation details as well.However still a Good Course of the Specialisation.

автор: Varada G

22 июля 2017 г.

It is a bit dense - be prepared to spend more time working through examples - and reading the reference book. The lectures, unlike the previous ones in this set, does not allow time for you to practice with the examples in jupyter notebook.

автор: Sparsh B

8 июня 2020 г.

This course was really helpful in understanding the working of various machine learning algorithms.

I was able to gain understanding of various evaluation techniques and there usage in different scenarios.

Thank you for this wonderful course

автор: Mark S

1 сент. 2020 г.

Lots of useful information, but sometimes the content could have been better explained. Too many errata than necessary in the assignments at the end of each week. I found that the Jupyter notebook would stop working after about an hour.

автор: Xuening H

29 янв. 2020 г.

Pro: I really like all the homework. The data is dirty and the work is a little bit challenging but doable.

Con: I prefer more animation in slices during the lectore to keep me concentrated. I get distracted watching the lecture's face.

автор: Marshall

18 дек. 2019 г.

I learned a lot about machine learning with python and would definitely recommend for someone with decent python background.. Some of the assignments have some very unnecessary technical hurdles that are unrelated to the material.

автор: Vinicius G

20 нояб. 2017 г.

Very hard but worth it. I only took one start off because I did not like the professor. Very sleepy voice and not very exciting explanations. Material was excellent and very helpful for the completion of assignments and quizzes.

автор: Shivam T

2 мая 2020 г.

I completed this course in specialization and this is the only course which is worth of your time, rest two before this course were your head against a wall.

Excellent course with all the understanding a student need.

Thanks :)

автор: Nicolás S C

28 июля 2018 г.

Really good and applied course. It teaches you a lot of powerful tools for machine learning.

The only negative thing is that the week 4 cover hard topics, and the explanations are vagues sometimes, but nothing too terrible.

автор: Caspar S

1 мая 2020 г.

Very happy with the course content.

On the other hand, certain instances need to be updated/corrected.

For several assignments, the files don't load and you need to dig through the forums.

It would've been 5 stars otherwise.

автор: Gourav S

28 дек. 2019 г.

It can be more detailed. It is on broader terms only. I will recommend Andrew Ng ML course to do as well because it covers too many things than this module. Otherwise, this is a good module as well. :) Enjoyed doing it.

автор: Qitang S

6 мар. 2019 г.

Good Introduction Courses, but need more guidance for assignments as there is a gap between two of them. Assignments do need some more hours to finish. In all, a great course for anyone to break into machine learning.

автор: Cat-Tuong N

2 окт. 2020 г.

Challenging and fun course. The number of topics is on the high side. Maybe break this into 2 courses? The programming assignments are fun. You will need to go to discussion forum to solve often encountered problems.

автор: VenusW

31 июля 2017 г.

Much better than the second course, the materials are carefully prepared and organized, teaching staff are very helpful in solving issues, however, assignments are not so challenging, still needs improvement.

автор: john w

29 янв. 2018 г.

Comprehensive and interesting course in Machine Learning. The use of Scikit Learn helps to give a concrete understanding of ML as well as how many specific algorithms can be utilized in real world problems.

автор: Vishal S

23 июня 2018 г.

It's a nice course. It'll familiarize you with different models, evaluation metrics and basics of machine learning and let you practice with some of the real world datasets during assignment.

автор: Muzahidul A

7 июля 2020 г.

assignments were so good. I think there was not enough information given for the quiz tests. And also the code given was not properly explained. But the materials were so good for practice

автор: Raul M

28 апр. 2018 г.

A good introduction to algorithms available in python. I didn't give it a five stars because I 'm still confused on which algorithms to pick/use when I want to work on real data problem.

автор: Julien Z

6 мая 2020 г.

Very good mix of video and python notebook. Some improvement can be done with the AutoGrader like get back the error python stack trace.

Globally, very good course - strongly recommanded

автор: Kai K

4 июня 2018 г.

The final assignment passing was a little too east,

there not being need to use fully what I learnt.

Still,the overall course was very good, and I am willing to keep on take other courses.