LN
The instructor has a great teaching style. I have enjoyed his sense of humour throughout the course. All the details are explained clearly and thoroughly by written notes or verbal explanation.

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.

LN
The instructor has a great teaching style. I have enjoyed his sense of humour throughout the course. All the details are explained clearly and thoroughly by written notes or verbal explanation.
PP
Nice and Helpful course for Begineers..Thanks to Team
SS
A very informative and well guided short session to understand overview of Classification Trees. Covers lot of important concepts in 1 hour. Highly recommend
YA
Awesome Instructor! Like this course. It clears basic knowledge about DecisionTreeClassifier, Tree Pruning, Dealing with missing Data etc.
ZA
الشاشة جدا صغير اضطر اعمل تدريبيا على كمبيوتر اخر حتى استطيع التركيز
MS
Good Course. Cost Complexity Pruning explained nicely. Bammmm!!!!!!!!
AS
Liked, easy to understand and utilize the knowledge in a similar dataset.
KD
This is a great course. The instructor does a wonderful job of explaining concepts and providing useful code.
HA
great class, but some of the code is out of date.
II
Good platform to learn about this type of project.
RR
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.
KK
Machine learning algorithms used for data-set classification and many more works really impressed.
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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!
new to learn.useful
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"
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.
Really awesome !!! Understood using Classification tress end to end in one go. Really thankful for Josh to have created the notebook with all theory required to understand writtent in it, good to revisit in future as well .
The instructor has a great teaching style. I have enjoyed his sense of humour throughout the course. All the details are explained clearly and thoroughly by written notes or verbal explanation.
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.
A very informative and well guided short session to understand overview of Classification Trees. Covers lot of important concepts in 1 hour. Highly recommend
Awesome Instructor! Like this course. It clears basic knowledge about DecisionTreeClassifier, Tree Pruning, Dealing with missing Data etc.
This is a great course. The instructor does a wonderful job of explaining concepts and providing useful code.
Very good and clear project, ideal to imporve knowledge in supervised learning and decision trees.
Machine learning algorithms used for data-set classification and many more works really impressed.
Liked, easy to understand and utilize the knowledge in a similar dataset.
Good Course. Cost Complexity Pruning explained nicely. Bammmm!!!!!!!!
الشاشة جدا صغير اضطر اعمل تدريبيا على كمبيوتر اخر حتى استطيع التركيز
Nice and Helpful course for Begineers..Thanks to Team
Good platform to learn about this type of project.
great class, but some of the code is out of date.
All the code and concepts were clearly explained.
Nice basics of scikit-learn DecisionTrees