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Avis et commentaires pour d'étudiants pour Data for Machine Learning par Alberta Machine Intelligence Institute

4.3
étoiles
79 évaluations
23 avis

À propos du cours

This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute....

Meilleurs avis

PN
29 déc. 2020

Excellent depth in coverage. Lab, although only one, was instructive to enable learning while also being exhaustive and intensive to drive learnings home.

BS
11 oct. 2020

Some bugs in the assignment, but overall excellent discussion of how to avoid common pitfalls when using data for ML.

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1 - 23 sur 23 Avis pour Data for Machine Learning

par Emil K

22 mars 2020

The instructor is great, but please fix the programming assignment! There are so many typos it's embarassing. Also, the autograder EXPECTS typos in some variable names, so you can't even pass it if your answers are correct.

par Hari N L

26 juin 2020

The experience with the programming assignment was very bad. There was an error that was occurring at frequent intervals which crashed my jupyter notebook, making me to start afresh. I was facing an issue in reopening the notebook where it took a long time and the mathematical notations were also not loaded properly.

par Hen H

16 févr. 2021

The lab should be broken down into 3 labs.. it was very long..I wish there would be more hands on practice like that lab! good stuff! jupyter notebook is a great tool..I really enjoyed this course, thank you !

par Andres L

31 déc. 2020

You'll learn how to be aware of your data and address different problems that could significantly affect your machine learning model. Plus, the practical assignment was really enjoyable.

par Prasad A N

29 déc. 2020

Excellent depth in coverage. Lab, although only one, was instructive to enable learning while also being exhaustive and intensive to drive learnings home.

par Brett S

12 oct. 2020

Some bugs in the assignment, but overall excellent discussion of how to avoid common pitfalls when using data for ML.

par Gustavo I M V

14 févr. 2021

This is a great course. In fact, the theory was amazing. I´m very glad with you, I can understand the data better.

par Emilija G

9 janv. 2020

The whole specialization is extremely useful for people starting in ML. Highly recommended!

par Camilo C

5 juil. 2020

Good course, if you follow the previous ones and if you know some python (Pandas).

par Miguel A S M

1 déc. 2019

What is different about this course is its focus of ML applied to the real world.

par Naruki H

17 juil. 2020

Excellent content with good programming assignments and examples.

par Tony J

17 juil. 2020

This is the best!!!

par Valery M

31 mars 2020

Nice course!

par Pankaj Z

10 mars 2021

This course is very helpful if you want to learn Machine Learning. The primary objective of the course is to ensure you make proper decisions while handling your data. This course walks you through different types of data, problems surrounding it and how to tackle them. It's one of the finest courses on data. I do hope the instructor adds some coding tests on handling data.

par Eshani A

28 nov. 2020

It's a really nice course covering all the content related to data in Machine learning. The content is so detailed and the instructor have made the entire learning process very smooth. Thanks a lot for such a great course.

par Pratama A A

8 juin 2020

Well this course absolutely good,but you need patience when doing programming assignment,and there's a lot error tho,but what we need is that information,anna gave us the easiest insight

par SHREYAS C

12 juin 2020

Really good,... one thing you have to change is that your assumption of people knowing Python for Jupyter Notebook really well... the week 3 assignment was a pain for quite sometime

par Abdullah A

24 déc. 2019

the course is very powerful and I have jump to higher level regarding data wrangling and how to deal with data. the assessment have some error which can be fixed easily

par Kham H Y

31 oct. 2020

The programming assignment was tough, the instructions were a bit misleading. I didn't get all correct though.

par Danijel T

22 juil. 2020

The instructor is knowledgable and materials are moderately useful.

Notebook with assignment is broken. There are many typos and elements which are not rendered properly. Notebook is huge and every subtask depends on previous state. It takes time to reload all previous tasks if you did not solve everything in one go. Final quiz basically repeats all the questions from previous quiz.

Course could use more polish.

par Halil T

18 sept. 2020

deeply theoretical but excellent assignment file (good review for pandas library )

par Jhon F B L

25 avr. 2020

The course is great but the courser a notebooks were a nigthmare

par Lam C V D

26 août 2020

Bad Grader system and complicated coding taught. Instructions given unclear and no instructor support at all.