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미시건 대학교의 Applied Machine Learning in Python 학습자 리뷰 및 피드백

4.6
별점
8,053개의 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

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.

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 1,465개 리뷰 중 151~175

교육 기관: Callum Y

2020년 2월 11일

It was a good introduction to machine learning. The assignments and quizzes were well designed to encourage self-learning, which in my opinion is one of the most valuable skills an aspiring data scientist could learn. All in all I am very satisfied with the course and I look forward to enrolling in the other courses in the specialization.

교육 기관: Ashkan S

2022년 7월 30일

I've never learned this much in 4 weeks. I studied more than 4 hours a day to keep up with so much new information.

Videos are great and professor Collins-Thomson does an amazing job teaching these courses.

Althogh assignments are extremely hard and a little unbalanced, I belive this is one the best courses I've ever had.

thank you so much.

교육 기관: ISAAC E

2022년 5월 14일

Giving a solid understanding of Machine Learning in Python by utilizing the scikit-learn library. Although, there are some limitation due to the online platform, the 'Discussion Forums' really helps in those problem. Overall, I enjoyed enrolling this class. Looking forward for any new classes which dive deeper in Applied Machine Learning

교육 기관: Binil K

2017년 7월 10일

This is a very nice course in Applied Machine Learning. For getting the most out of it, it would be nice to have taken ML Specialization from Andrew Ng which will take a deep divce into the working of ML models or have good amount of knowledge in ML. Having familiar with ML concepts, you would find this course really useful.

Regards,

Binil

교육 기관: Pranav S

2020년 7월 2일

It was great learning experience.This course exposed me to various parameters of machine learning using python programming and helped me to gather knowledge about the significant use of Pyhton Programming in the field of machine learnig.Pandas,Regression topics are rightly and deeply understood to me because of this course.

THANKYOU!!!

교육 기관: Ahmed M R

2022년 3월 27일

T​his Course gives a very good understrandig of the solid basics of Machine Learning that anyone looking to touch basis on this topic for a career progression would find very beneficial. It describes the core concepts in the abstract level that is needed to know as a kickstart, providing a great optional material and community forum.

교육 기관: Mustafa K

2017년 9월 22일

This is the most useful machine learning course in the internet. It helped me to understand machine learning algorithms very well that I never saw in other courses. This course covers most of the machine learning algorithms that needed nowadays. Thanks to Michigan University and Coursera to make this course to be available online.

교육 기관: Zhao H

2018년 6월 21일

Highly recommended. Great practical overview of machine learning approaches.One shouldn't expect the underlying implementations from this course due to the time strain - only a few weeks, and should take Andrew Ng's machine learning class for that.To go even deeper for some methods, one should take more machine learning classes.

교육 기관: Martyna S

2017년 11월 16일

Very interesting and engaging course. I liked graphical comparisons of different models and their params. Module notebooks were very handy while doing assignments. All homeworks were not trivial, developing and demand attention to detail. Big plus for teachers posts at forum - they help a lot while doing quizzes and assignments.

교육 기관: WILVER S Y

2021년 5월 1일

It is a wonderful course that convers a basics of Machine Learning, the instructor provides an excellent explanation of the topics and the Jupyter Notebooks helps you understand and document all the concepts learned through the course. If you want to build some knowledge in ML this is a good option to start this great journey!

교육 기관: Steve M

2018년 4월 15일

An excellent overview of current machine learning knowledge and practices. This course is very information dense and requires additional reading and time for the assignments. It is challenging for an 'intermediate' level course. Some prior knowledge of machine learning is recommended, and strong Python skills are required.

교육 기관: Juan D

2020년 6월 15일

Very applied course, while still teaching you the basic concepts. You can start using machine learning solutions to your problems right away with confidence. The course covers a lot of ground, so expect some topics to be treated rather superficially. It provides a lot of material if you want to expand your knowledge though.

교육 기관: Lewis M

2019년 1월 13일

Very good course for either an introduction to machine learning or to refresh old skills. It's also very good at putting emphasis on topics that data scientists may overlook / not pay much attention too, so having this as a reminder to look deeply into each algorithm and its application or limitations is incredibly helpful.

교육 기관: Stephan K

2019년 5월 19일

excellent, practical introduction to (mainly) supervised machine learning in scikit learn. Next to Python specific handling of models, also conceptual issues like parameter tuning, feature pre-processing and - very nicely - data leakage are explained. examples can get tricky without solid grasp of numpy and pandas packages

교육 기관: 王桢

2017년 12월 3일

this is an interesting machine learning course

can quickly understand the basic idea of machine learning and know how to build different models in python and select models based on different standards

it is a very good course to start with machine learning and can arouse the interests of learning more in this emerging field

교육 기관: Davide P

2019년 5월 11일

The course covers a many topics of the ML world.

The exposition of the arguments is well organized.

The assignaments and quizzes are difficult enough to force you to really understand the lessons and learn the arguments but are not impossible to be accomplished.

The teacher are always ready to help you in the course forum.

교육 기관: Gowri T

2020년 5월 3일

Good course, but take it with a theoretical course also, (I suggest Learning from Data, Caltech, the lectures are on youtube and assignments are put up online). This one goes well with it, because LFD teaches to code up classifiers and regressors without libraries and this one teaches us practical use of scikitlearn.

교육 기관: lvbart

2018년 4월 29일

this course may be the most challenging one I have ever met, those concepts and examples I have never thought would met in my life. but after intense learning and excellent course arrangement, I may get a little sense of machine learning now.

Thanks for the great job, dear applied machine learning in Python team!

교육 기관: Sahir N A

2017년 6월 30일

I did this course only from the entire specialization so it was a little hard to catch up but the difficulty made me even more excited to keep going and finish every bit of the course. I really appreciate the amount and quality of content, quizzes and assignments. Totally worth my time. Thanks UoM and Coursera!

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.