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Share Dialog
Share Dialog
By reorganizing my teaching materials, from some of excellent blogs and papers, I have drawn a mind mapping to present those knowledge intuitively. This mind mapping is designed to make my readers take lesser time to recall more useful basic information. More specifically, I will make it as a quick guide map, reduce the use of mathematical formulas as much as possible for ease of understanding.
For the first version of mapping, this mapping is divided into three parts: data preparation, prediction and clustering. The two main categories are prediction and clustering which mainly describes what it is?(in the remarks) How it is? when to use? For each proper noun, I attach a picture (which is a better presentation) for your reference.

This is the whole structure and mind mapping. After downloading it, you can see each model picture by zooming in the mind mapping.

Important(Must read):
Make sure you have downloaded vector graphics(svg file) from the link, otherwise the model picture will not be clear enough after zooming in. If the link expired, please contact me by LinkedIn(click here).
SVG file Download from here:
https://drive.google.com/file/d/1qAZlH3tw6qSlCZ8zw_9Jhs98Ntyjv6xX/view?usp=sharing
If you like or think this will help you, just Click Like or retweet to encourage my future work. If you don’t like, leave your comment on LinkedIn post or Twitter post to let me know. Cheers, Love you guys!
Future work:
collect more advice to fix and improve
add more details about data understanding and data preparation
add clustering measure method
add development docs link
References:
DB Skillicorn, (September 2021) CISC251-Data Analytics
Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems?. The journal of machine learning research, 15(1), 3133-3181.
LinkedIn:
https://www.linkedin.com/in/zhe-zhang-358095184/
Donate to this address, you can slap author’s ass and buy him a coffee:)))
0x9048789C5c27b02Ca437d0E965BB557E55A34658
By reorganizing my teaching materials, from some of excellent blogs and papers, I have drawn a mind mapping to present those knowledge intuitively. This mind mapping is designed to make my readers take lesser time to recall more useful basic information. More specifically, I will make it as a quick guide map, reduce the use of mathematical formulas as much as possible for ease of understanding.
For the first version of mapping, this mapping is divided into three parts: data preparation, prediction and clustering. The two main categories are prediction and clustering which mainly describes what it is?(in the remarks) How it is? when to use? For each proper noun, I attach a picture (which is a better presentation) for your reference.

This is the whole structure and mind mapping. After downloading it, you can see each model picture by zooming in the mind mapping.

Important(Must read):
Make sure you have downloaded vector graphics(svg file) from the link, otherwise the model picture will not be clear enough after zooming in. If the link expired, please contact me by LinkedIn(click here).
SVG file Download from here:
https://drive.google.com/file/d/1qAZlH3tw6qSlCZ8zw_9Jhs98Ntyjv6xX/view?usp=sharing
If you like or think this will help you, just Click Like or retweet to encourage my future work. If you don’t like, leave your comment on LinkedIn post or Twitter post to let me know. Cheers, Love you guys!
Future work:
collect more advice to fix and improve
add more details about data understanding and data preparation
add clustering measure method
add development docs link
References:
DB Skillicorn, (September 2021) CISC251-Data Analytics
Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems?. The journal of machine learning research, 15(1), 3133-3181.
LinkedIn:
https://www.linkedin.com/in/zhe-zhang-358095184/
Donate to this address, you can slap author’s ass and buy him a coffee:)))
0x9048789C5c27b02Ca437d0E965BB557E55A34658
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