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Opiniones y comentarios de aprendices correspondientes a Applied Machine Learning in Python por parte de Universidad de Míchigan

6,854 calificaciones
1,239 reseña

Acerca del Curso

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....

Principales reseñas

8 de sep. de 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

13 de oct. de 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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1176 - 1200 de 1,219 revisiones para Applied Machine Learning in Python

por Fernanda T

4 de ago. de 2020

Good content and I learned a lot. However, the instructor made too many mistakes during the lectures and the assignments also have mistakes that need to be fixed by the students.

por Ketan L

4 de jun. de 2018

Follow the course with introduction to ML with python to have descent understanding. Instructor won't be able to keep one interested for long. Exercises could have been tougher.

por Victor E

16 de ago. de 2017

Two point: 1) you can learn a lot here, 2) imagine you are shown a hammer but never explained how to hit a nail. Two previous courses in the specialization do both.

por Kareem H

3 de mar. de 2020

Course instrutor and materials are needed to be improved as they are very poor

Assigments\Quizes are very good and they are the mainly root cause for this rating

por Thomas B

7 de jul. de 2018

Some very good practical advice like dummy testing or data leakage issues Some trivialities and repetitions. Python code could have been a bit better commented


13 de jun. de 2020

there should be some low level usage of sentences for a intermediate programmers,most of times it bounces up the mind ,not able to get the required concept

por Baizhu

5 de jul. de 2017

Know some existing machine learning functions and packages from sklearn, but really don't know how to improve prediction accuracy within each function.

por Matteo B

10 de ago. de 2019

Assignments are not really supported by the material provided (videos). The level is not balanced. Some bugs in the assignment code as well

por Berkay A

15 de jul. de 2020

This course seems hard and actually I did not like the syllabus so much. Assignments were so hard and there were some issues in Notebooks.

por Halil K

26 de sep. de 2019

Good content, bad teachng staff. Though the discussion forum contributors were very helpful and should be commended for their efforts.

por Ankur P

30 de mar. de 2019

Unsupervised learning was missing. The codes written in the lectures were not explained clearly. Some topics looked unimportant.

por James F

13 de feb. de 2018

Good overview of methods. A bit too intense at times though, may have been better to really focus on a couple of key concepts.

por Om R

26 de abr. de 2020

The course is great, but need certain improvement for assignments and quizzes. The facts should be checked multiple times.

por Darshan S

31 de dic. de 2019

Not enough real life examples throughout the video, makes it very hard to concentrate during the whole lecture.

por Mauricio A E G M

17 de nov. de 2019

This course is not useful to learn from scratch, but has some good things, for example the final assignment.

por Nikola G

14 de ene. de 2019

Really didn't like the quiz parts of the course. If it was up to me I would do thorough revision of these.

por Chirag S

24 de may. de 2020

The content was less informative and audio quality was poor. However, assignments are fun completing.

por Rohit S

21 de may. de 2020

The online grader needs to be updated as there is constant error showing up though our code is right

por Gilad A

27 de jun. de 2017

The last assignment was super. apart for it, the assignments and the course were too easy

por Sai P

3 de jun. de 2020

There were a few corrections made during the videos which ended being quite confusing.

por Philip L

31 de oct. de 2017

The assignments are extremely difficult, professor is a bit dry during lectures.

por Pakin S

10 de ene. de 2020

How can i pass without reading discuss about problem with notebook

por Hao W

27 de ago. de 2017

The homework is too easy to improve our understanding of ML

por M S V V

29 de jun. de 2020

Too much of information compressed within a short span.

por José D A M

21 de jun. de 2020

Too fast, yet too difficult. Needs deeper explanation.