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Avis et commentaires pour d'étudiants pour Machine Learning Algorithms: Supervised Learning Tip to Tail par Alberta Machine Intelligence Institute

4.7
étoiles
401 évaluations

À propos du cours

This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, 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 second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute....

Meilleurs avis

TH

14 mai 2022

This is an excellent course which goes into some depth on the different ML models and underlying complexity but it avoids getting bogged down into the details too much.

DS

6 mai 2020

Excellent course for an overview of different ML algorithms. The course is made from a perspective of giving insights in process and not too many mathematical details.

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51 - 64 sur 64 Avis pour Machine Learning Algorithms: Supervised Learning Tip to Tail

par Morgan S

23 mai 2021

This course is a great overview of ML concepts. The professor is superb! I did not give 5 stars because the labs need to be improved. The labs are too simple. This course should provide more opportunities for applying the ML concepts.

par Andrey Z

29 déc. 2021

G​ood overview course. I hope authors will add more practice task in the futrure

par Kham H Y

28 oct. 2020

Learn some valuable insights on scikit-learn capabitlity through the labs

par nouran a

7 mai 2020

Many useful information but need some more explanation, overall awesome

par Saksham G

4 avr. 2020

More maths to explain the underlying concepts will be good!!

par Daniel W

28 nov. 2020

Machine learning concepts are introduced well.

par Grecia P

3 mars 2020

week two was heavy

par MARRU R

18 déc. 2020

nice

par sandeep d

27 août 2020

nice

par Nicolas G

17 avr. 2021

High level overview of Machine Learning, poor examples and incomplete labs.

par PIYUSH G

8 avr. 2020

good

par Raghuram T

10 oct. 2020

It could have been better if the trainer had included more hands-on examples rather than just tuning the slides and most important algorithms and its usage was there in the attached links for reading and I felt if this could have been taught in the training rather than just a document to read and self learn the quality of the course would have been fantastic.

par Sara K

30 sept. 2021

The instructor is wonderful. She does not sound like a robot/reading off of cards like so many other instructors do. However, the practice assignments are still in draft form and missing files you need in order to complete them. That is why I gave this a one star.

par Carlos E

12 oct. 2021

D​oesn't explain in a good way the models used. Only explain how to use them. I really need the math and a deep explanation to understand how to use the function. This is like they just give you the documentation and do examples.