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Bewertung und Feedback des Lernenden für Applied Machine Learning in Python von University of Michigan

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Sterne
7,981 Bewertungen
1,452 Bewertungen

Über den Kurs

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....

Top-Bewertungen

OA

8. Sep. 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

AS

26. Nov. 2020

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.

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326 - 350 von 1,442 Bewertungen für Applied Machine Learning in Python

von Aneek A

30. Sep. 2017

Very informative and covers Machine Learning (along with scikit learn) in great breadth!! Would love to see a bit more challenging assignments though.

von Alexandre G

24. Okt. 2019

This is a very good course. Probably, much time should be given, especially for Week 2 and Assignment of Week 4. Thank you very much for the course!

von Surya P M

1. Apr. 2019

complex topics are explained in a simple way. coding assignments, quiz helped a lot to learn and apply numerous machine learning concepts perfectly.

von Jay N

18. Okt. 2018

very very excellent, got to learn whole lot of machine learning models and approaches. i'm straight away going for kaggle competitions after this.

von Carlos F P

20. Sep. 2018

It gives a great overview of different machine learning methods. I found useful information that can be missing in other ML courses. Great course!

von DESHPANDE J S

9. Juli 2017

I am a beginner in Machine Learning. I find this course very easy to follow, interesting and informative. Thank you for the efforts you've put in!

von Jack R B

23. Sep. 2020

Great course. A LOT of information but great job at teaching conepts and how to apply them. It got me really interested in Deep Learning and MLP.

von Lucas G

5. Juni 2017

Great course! Really appreciated it, it taught me (and gave me lots of practice) how to use lots of different classifiers for machine learning.

von Manik S

8. Feb. 2019

Optional references to the inner workings should be provided. For example how Decision Trees are trained and how the best division is decided.

von Bruno S F C H S

5. Juli 2020

Excellent course to do an overview of many ML algorithms, and with good assignments that help me to fix all the subjects that I have learned!

von Ari S P

11. Mai 2020

From several MOOCs that focus on ML. I love this course to understand the fundamental off ML and I can easily apply this course in my project

von Zijie L

30. Aug. 2018

Easy for beginner to follow. After finishing the course,I'm able to apply simple machine learning algorithms to area I'm currently working on

von James A

16. Mai 2021

This was the best course in the specialization, in my opinion. I think I learned the most and got the most value from the materials in here.

von Aino J

21. Juni 2020

Practical, applied, and a good overview of how to apply different (mainly supervised) machine learning algorithms using python scikit-learn.

von Santhana C

5. Aug. 2017

Nice Course! Lots of useful information packed in 4 weeks. Be prepared spend some extra time if you want to really benefit from this course.

von Eddie G

18. Jan. 2021

This course has the perfect combination of theory and practice. It's Intense for a beginner In machine learning but Is absolutely worth It.

von Rajendra S

11. Jan. 2019

This course is the one that I enjoyed most while learning anything in Coursera. Thank you everyone associated with this course and content.

von Juan R C C

25. Okt. 2017

Good course, content and teaching. Very good weekly assignments allow students to well consolidate course contents on real world practices.

von Nattapon S

3. Aug. 2017

It is a good class. I learn a lot from this course. It is a concise starting course for Python machine learning. I recommended this course.

von JOSE A P A

14. Juli 2020

Un excelente curso para reforzar lo aprendido en el curso Minería de Datos para la Toma de Decisiones que se dicta en la Universidad Esan.

von Ramon S

7. Dez. 2020

Excellent! I had previously done a course on machine learning and it left me with big holes in my knowledge, this really clear things up!

von Moustafa S

28. Juli 2020

GREAT COURSE!, this is one of the greatest courses for applying machine learning and data science algorithms and skills, great great job.

von Ritesh P N

19. Juli 2020

It was amazing course for applied machine learning. The tutor was good teaching core concepts of machine learning algorithms step by step

von Wallace T

8. Sep. 2018

Great Course with high practicality. Need more lectures on how to process categorical data. Read the Forum if you encounter any question!

von Fengping W

28. März 2018

It is really a good one, and I learn a lot here, both for theory and applied skills. And the reading materials are really good resources