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Вернуться к Classification Trees in Python, From Start To Finish

Отзывы учащихся о курсе Classification Trees in Python, From Start To Finish от партнера Coursera Project Network

4.6
звезд
Оценки: 219
Рецензии: 45

О курсе

In this 1-hour long project-based course, you will learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with Python and the theory behind Decision Trees, Cost Complexity Pruning, Cross Validation and Confusion Matrices. Notes: - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

Лучшие рецензии

RR
17 авг. 2020 г.

Josh Starmer's videos and courses are always simple and easy to understand. Thank you for this wonderful course. I will definitely recommend everyone to take this course.

SS
17 июня 2020 г.

A very informative and well guided short session to understand overview of Classification Trees. Covers lot of important concepts in 1 hour. Highly recommend

Фильтр по:

1–25 из 44 отзывов о курсе Classification Trees in Python, From Start To Finish

автор: Erick M F d S

15 мая 2020 г.

Short, but good content. Still lots of problems with the Rhyme platform.

The main problem with the guided project was that the Rhyme platform is still problematic: the video playback was constantly being interrupted for buffering, especially at higher playback speeds (my internet connection is good enough for 4k streaming), the cloud desktop for the Jupyter notebook is quite laggy, doesn't allow copy and paste between cloud and own computer; the whole UX of a single browser window for both video and remote desktop is very awkward and inflexible; the video playback was paused every time the browser window was out of focus, as when I was writing some notes on another window. Finally, I couldn't easily download the completed code, for use in my own projects, thus reducing my capacity to reuse what was learned without extensive notes.

Guided projects are a great idea. Not sure I would pay U$ 10 for simple projects when there are similar excellent code freely available on Kaggle or github, but Coursera's selection of content might make it worth. But the current performance of Rhyme is still insufficient for a paid service. I can get better service out of Google Colab, for free!

автор: Joseph j D

16 апр. 2020 г.

new to learn.useful

автор: Киселева К К

22 нояб. 2021 г.

After the first 5 seconds I've felt something was wrong and missing. And suddenly I realized what it was. "Hello, and welcome to STAT QUEST!" I am a huge fan of the lecturer's Youtube channel, he is the best statistics lecturer I've ever heard. Was not disappointed by this practical project. His explanations are always like "ba-am, that's so easy"

автор: Maria B

14 июня 2020 г.

I love Josh Starmer's teaching style. He's definitely one of the best teachers I know. I will always recommend his work. However, I would have enjoyed the course a little more if he had expanded his window in the Rhyme platform, the size of the screen makes it hard to follow sometimes.

автор: Rahul R

18 авг. 2020 г.

Josh Starmer's videos and courses are always simple and easy to understand. Thank you for this wonderful course. I will definitely recommend everyone to take this course.

автор: Sagar S

18 июня 2020 г.

A very informative and well guided short session to understand overview of Classification Trees. Covers lot of important concepts in 1 hour. Highly recommend

автор: Yasir A

14 сент. 2020 г.

Awesome Instructor! Like this course. It clears basic knowledge about DecisionTreeClassifier, Tree Pruning, Dealing with missing Data etc.

автор: Karna D

25 авг. 2020 г.

This is a great course. The instructor does a wonderful job of explaining concepts and providing useful code.

автор: Alvaro V

26 июля 2020 г.

Very good and clear project, ideal to imporve knowledge in supervised learning and decision trees.

автор: KALPANA

10 мая 2020 г.

Machine learning algorithms used for data-set classification and many more works really impressed.

автор: Anand S

28 июня 2020 г.

Liked, easy to understand and utilize the knowledge in a similar dataset.

автор: Mayank S

2 мая 2020 г.

Good Course. Cost Complexity Pruning explained nicely. Bammmm!!!!!!!!

автор: ZAINAB S I H A

22 июня 2020 г.

الشاشة جدا صغير اضطر اعمل تدريبيا على كمبيوتر اخر حتى استطيع التركيز

автор: Punam P

16 мая 2020 г.

Nice and Helpful course for Begineers..Thanks to Team

автор: IMRAN H I

28 авг. 2020 г.

Good platform to learn about this type of project.

автор: coding s

14 дек. 2020 г.

All the code and concepts were clearly explained.

автор: Szymon K

18 июля 2020 г.

Nice basics of scikit-learn DecisionTrees

автор: Sagar P

30 июня 2020 г.

Good course to learn classification Tree

автор: Carlos A P

25 окт. 2020 г.

Good intro to Classification problems

автор: Kodhai.E

22 мая 2020 г.

Best Hand-on training by course

автор: SUGUNA M

23 нояб. 2020 г.

Good project based course

автор: Rati K J

8 июня 2020 г.

IT WAS BETTER EXPERIENCE

автор: Gangone R

2 июля 2020 г.

very useful course

автор: Nikita D S

25 апр. 2020 г.

Its very useful...

автор: Akshit B

1 сент. 2020 г.

Just excellent.