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

4.6
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7,413 calificaciones
1,351 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

OA
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

AS
26 de nov. de 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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26 - 50 de 1,331 revisiones para Applied Machine Learning in Python

por Olubisi A

10 de ene. de 2019

I think this course would be a bit challenging to someone who is new to machine learning. The professor often glosses over import details and moves a bit quickly through the course material. There needs to be more powerpoint and reading material explain what the videos explain.

por Amir A C

19 de ene. de 2020

Unfortunately, for me, this course (not the specialization) seems to be a "review of" Applied Machine Learning in Python" rather than "teaching" Applied Machine Learning in Python. Some codes used in the notebook were skipped by the instructor.

por Mahmoud

28 de dic. de 2018

Week three is the worst ..

Lecturer is getting confused a lot in an already confusing topic which ofc makes me resort to outside readings in order to grasp it and leading to stretching the time I need to finish this week

por Gregory B

14 de jun. de 2017

I'm disappointed that I took this class, poor design and delivery. Machine Learning is an exciting and fun topic, but you'd never guess it from this class, and the way the instructor delivers the content. It's a shame that the designers want to throw every possible model at you in 1 or 2 weeks, before having a discussion on model evaluation. This course focuses more on the academic than the practical, and doesn't try to explain these topics in an approachable manner. There are far better and engaging options available.

por Saqibur R

3 de may. de 2020

This course is all over the place, and compared to the previous courses in this specialization, this seems like more of an effort to gloss over the documentation and capabilities of SciKit Learn rather than focusing on a handful of the most important ones. The course lacks focus, the material taught is not rich, and you are better off just reading the documentation on your own. The book recommended at the start of the course is excellent, and reading that instead might be more fruitful for you.

por Rishi R

6 de jul. de 2018

Rather then writing code while explaining like the intro and plotting in python, the instructor shows it like slides, its hard to follow which chunk of jupyter notebook he is explaining, and requires lot of back and forth to read the code. Very bad way of explaining the codes.

por Sean D

12 de jun. de 2019

This is the worst course in the specialization. The autograder is bad. There is inadequate explanation about when to use the different models. Presumes way too much about the student's level of knowledge. Would not recommend.

por Sudhir J

17 de feb. de 2020

Very poor configurations. I am tired of submitting assignments on auto grader. This is the first time I am having such terrible experience with Coursera. Hope you improve.

por Ipsita D

20 de abr. de 2019

No visible support from groups forum. Videos knowledge is limited to complete assignment or quiz.

por shaoqi c

10 de mar. de 2020

This is my worst experience of submitting assignment and I found out that I'm not alone

por Amber K

21 de jul. de 2020

The guide could not explain the concepts well. He was just reading from the slides.

por Pei L

27 de jun. de 2020

Bad teaching, unclear explanations.

I learned half of the material from Youtube.

por Nomthandazo T

2 de mar. de 2020

this is the worst course ever. so bored and frustrated

por Edward G

24 de ene. de 2018

Terrible quiz problems and grading mechanism

por Vaibhav S

26 de jun. de 2018

This course provides a brief introduction to many of the vast and dense ML concepts, like Regression, Classification, Clustering, Neural Networks and many more.I took a course by Prof. Andrew ng on Coursera before taking this course. And due to this reason, i was somewhat familiar with the concepts that are being taught in this video.If you are a beginner, i personally recommend you to take Prof. Ng's course on Machine Learning, and then switch to this part of specialisation, by completing the 1st specialisation (2nd is optional but if you are sort of artistic person, and have a habit of visualising things then opt this too). It is best for those who just want a quick recap of some topic.

por Pankajkumar S

4 de jun. de 2019

This is an excellent course. The programming exercises can be solved only when you get the basics right. Else, you will need to revisit the course material. Also, the forums are pretty interactive.

por Athira C

30 de ene. de 2019

The course is so informative and interseting.

por Pawan M

4 de may. de 2020

This is an excellent course. If you will complete all exercises making sure you complete all questions in each exercise and score almost 100% in each quiz then you will get full value out of course. Deadlines can be reset any time so you can resume courses anytime and you can take your own time as per your schedule. The programming exercises can be solved only when you get the basics right. Else, you will need to revisit the course material.

por Haim S R

27 de jun. de 2019

Gives practical experience with ML in Python.

Hides the math under the hood :(

However, this course is not enough to become a real data scientist. One needs much more exercises.

por Krishna B S

6 de mar. de 2019

A very comprehensive and hands-on course for learning applied Machine Learning. Many thanks for this course.

por Navish A

20 de jul. de 2020

I just completed the third course (Applied Machine Learning course) over the last 7 days.

Good:

The course syllabus is quite well designed for an applied intro ML course

Assignments are nice & force you to think; you cannot simply watch the lectures & complete them straightaway; which is good in my opinion.

Needs to Improve:

The lectures are atrociously boring. The professor seems to be reading out from a teleprompter in a flat pitch.

There are parts where the intuition behind the concepts are well explained and others where you are left staring at stars and better off learning from other sources over the net.

The course seems to have been all but abandoned. Common mistakes in the assignment setup & lecture recordings have not been corrected since the course was first offered 2.5 years ago. The discussion forums keep getting spammed on similarly asked questions which can be easily solved by correcting the assignment errors and providing a few clearer comments/instructions. Week 3 lectures definitely need to be re-recorded as there is a correction prompt on every video. There is one 'Mentor' who helps out as a volunteer. No one else to moderate the forums.

The course pace is quite uneven and patchy. Week 2 is extremely heavy while week 1 super light. Week 3 is good but week 4 feels half done/rushed. Seems like there is an arbitrary administrative requirement to do a four week course from UMich.

All in all, I did not come away impressed & elated from the course. I did expect much better from my Alma Mater.

por Shiomar S C

14 de oct. de 2019

Honestly this course was somehow disappointed I really wanted to learn a lot but the professor was somehow discouraging, he repeated himself a lot, and for an online course and every video been 20+ minutes long and at the end only been useful 4 or 5 min of it… having so much errors during lecture and not following the notebook as it was given to us make it more difficult to learn… I’m choosing this platform (and paying) due the professor been good and this one make learning more difficult than the previous one.

por Sajjad K

13 de jul. de 2020

Teachers are very mediocre. They make way too many mistakes. Their pronunciation is stoic and muffled at times - makes it hard to follow.

por fulvio c

25 de feb. de 2020

The video and training provided it's not providing enough information in order to complete the assignments.

por Rakesh D

10 de nov. de 2019

lectures are boring, not updated but yes i learned something, but its not up to the margin