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미시건 대학교의 Applied Machine Learning in Python 학습자 리뷰 및 피드백

4.6
별점
8,254개의 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!!

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

필터링 기준:

Applied Machine Learning in Python의 1,500개 리뷰 중 176~200

교육 기관: Praveen R

2019년 10월 29일

Lots of material to cover in this course. From supervised learning to the optional un-supervised learning schemes. A good introductory course to all theory there is to know on applied machine learning. The professor gives a glimpse of internal mathematics too. Interesting course, but lot of material to cover.

교육 기관: Iver B

2018년 5월 2일

An ambitious but systematic overview of a wide range of machine learning techniques using scikit-learn and other Python libraries. Prof. Collins-Thompson is a steady and clear explainer of somewhat complex topics. The exercises and quizzes can be challenging, but are very worthwhile.

Overall, very well done.

교육 기관: Andrew B

2020년 11월 1일

Good course. It's not heavy on math. This course is a good starting point for machine learning if you have basic python skills. I would recommend doing Assignment 4 in the online jupyter notebook that is part of the coursera course. The online jupyter notebook uses the same import versions as the autograder.

교육 기관: Jeroen D

2018년 6월 14일

Good introduction into the scikit learn package, took way more time than advertised but I also learned more than expected.I contrast to course 1, the assignments were easier, but the quizes were harder. Distribution of materials could have been better: week 2 has by far the most material to digest and learn.

교육 기관: Henryk S

2018년 12월 28일

I have been confidently guided through the complexities of Machine Learning through perfect mix of lectures and reading materials. Quizes and programming assignments served as very helpful tool to zoom in on specific details which in further assignments will make the difference between success and failure.

교육 기관: Leo C

2018년 2월 16일

Brief but in-depth introduction to many modeling methods and using them in python. It provides a great foundation for the rest of the courses in this specialization, but I wish other courses would be developed in collaboration with this intro course, rather than a series of independently designed courses.

교육 기관: Kshitij C

2023년 1월 28일

I would like to show my gratitude towards the instructor and of course the team that they really explained each and every part of the topic that it has really cleared all my doubts regarding the Machine Learning concepts.

Thank you University of Michigan for providing such an awesome knowledge to me.

교육 기관: Чижов В Б

2017년 11월 15일

Very interesting and informative! The material outlined in the course, difficult to understand, IMHO, but the organizers and the teacher managed to present it in an accessible form. Special thanks to Kevyn Collins-Thompson for his lectures and Sophie Grenier for her work and attention to the forum.

교육 기관: 251_NEELANJAN M

2020년 4월 6일

Coursera has made possible for millions of students worldwide to access the best quality of education through their medium. An opportunity to learn and develop as an individual changes a person's life substantially and most importantly Coursera is providing this opportunity to millions for free.

교육 기관: Sridhar I

2017년 12월 21일

A great crash course in some of the basics of machine learning on Python. Although not explicitly covered, the assignments helped me gain an understanding on the Jupyter framework & pandas.

The final assignment was definitely a cherry on top that let me gain a very vivid insight into the field.

교육 기관: Jakob P

2017년 9월 2일

Fundamental, but still thorough, course in applied machine learning using Python. The lecturer is really good, and the quiz/problem sessions are challenging, but sufficient information is provided in the videos -- a HUGE improvement compared with the first two courses in this specialization.

교육 기관: Youdinghuan C

2017년 6월 25일

This is a great course. Content is highly organized. The amount of lecture material was just about right. The professor is an excellent lecturer. Assignments and quizzes really helped reinforce my learning. If the Autograder is less demanding, this course would have been better in my opinion.

교육 기관: Andrew R

2019년 12월 24일

The Applied Data Science with Python specialization continues to deliver with Applied Machine Learning. Both quizzes and assignments are challenging but exceptionally well architected. I'm walking away with a great deal of beginner to intermediate skills in machine learning and scikit-learn!

교육 기관: Roger S

2020년 6월 15일

Gives a good overview on ML-Techniques. I liked the evaluation part. "Applied" means - they provide no technical/mathematical details of the different methods. You should get it somewhere else.

Everything is well set up. You need the knowledge of the previous courses of this specialization.

교육 기관: Rajan G

2020년 7월 6일

The course was very good. It has covered a lot of topics in a small time and has provided a good insights about all of them. It would be good if some hints can be provided with each question during the assignment as while facing confusion or problem it can help us to progress further.

교육 기관: Sumit M

2019년 2월 19일

This is a very good course about How to apply Machine Learning but I think before taking this course the student should take the Andrew Ng machine learning course by Stanford University to Learn the Important Mathematics behind the ML algorithms

But Enjoyed this course a lot

thank you

교육 기관: Abhishek B

2020년 5월 2일

The course definitely provided me with great insight. It allowed me to see different things & try out manifold elements in my own projects at work. Getting to know extensively on classification was really good. Just the only thing missing was the same depth for regression problems.

교육 기관: Mark H

2018년 2월 1일

Excellent course! Well paced lectures, challenging quiz questions that also require insight and understanding, and programming assignments with explicit instructions leading to very little auto grader frustration. The perfect python complement to Andrew Ngs machine learning course.

교육 기관: Bharath

2019년 6월 17일

Initially i had issues in getting in to video learning mode, got accustomed to it. One of the best way to learn in your own time as and when it suits you. Submission issues got sorted when discussed with peer. Maybe a SPOC for each course can be of more help to do it more quicker.

교육 기관: Kunal c

2017년 6월 21일

Wonderful course. The video lectures are very much to the point and this course is especially useful for someone who is more interested in application of Ml algorithms rather than their development. The intuition for all the algorithms are good and the course is very comprehensive

교육 기관: XL T

2020년 5월 21일

wonderful course. It requires a lot of self learning time to be honest. For my case, I have to do a lot of google search and background reading so to keep up to the learning pace of this mooc. However, I am very happy to be able to finish the assignments and it feels productive.

교육 기관: David H

2018년 8월 4일

Helped me to get the solid concept of Machine Learning. Since this course is mainly focused on the ways to use the machine learning skills in the real world problems, if you are interested in the mathematical approach of each skill, you might need to look into the other courses.

교육 기관: Subham B

2020년 6월 11일

Consider about buying this course if you have some pre-knowledge about ML....Understand that this is not a full ML Course, but a course that describes a lot about applications of this and different ML Algorithms. But this a very good course cause it does what it says very well.

교육 기관: Chrisada S

2018년 1월 2일

I really like that this course focuses on the application of machine learning methods, at the same time still provide enough insight of the working of each model. I do have the math background to follow the proofs, but I would rather spend my time doing rather than proofing.

교육 기관: Angadvir S P

2019년 2월 24일

The course was very useful, however, few of the assignments (specifically assignment 2) had a few errors in accurately displaying the question content and grading method was found to be slightly inconsistent with what was asked in the cells (Jupyter notebook).

4.5/5.0 stars