dformoso/machine-learning-mindmap

A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.


repo name	dformoso/machine-learning-mindmap
repo link	https://github.com/dformoso/machine-learning-mindmap
homepage
language
size (curr.)	15538 kB
stars (curr.)	4335
created	2017-08-10
license	Apache License 2.0

Machine Learning Mindmap / Cheatsheet

A Mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

Overview

Machine Learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data.

Machine Learning is as fascinating as it is broad in scope. It spans over multiple fields in Mathematics, Computer Science, and Neuroscience. This is an attempt to summarize this enormous field in one .PDF file.

Download

Download the PDF here:

https://github.com/dformoso/machine-learning-mindmap/blob/master/Machine%20Learning.pdf

Same, but with a white background:

https://github.com/dformoso/machine-learning-mindmap/blob/master/Machine%20Learning%20-%20White%20BG.pdf

I’ve built the mindmap with MindNode for Mac. https://mindnode.com

Companion Notebook

This Mindmap/Cheatsheet has a companion Jupyter Notebook that runs through most of the Data Science steps that can be found at the following link:

https://github.com/dformoso/sklearn-classification

Mindmap on Deep Learning

Here’s another mindmap which focuses only on Deep Learning

https://github.com/dformoso/deeplearning-mindmap

1. Process

The Data Science it’s not a set-and-forget effort, but a process that requires design, implementation and maintenance. The PDF contains a quick overview of what’s involved. Here’s a quick screenshot.

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2. Data Processing

First, we’ll need some data. We must find it, collect it, clean it, and about 5 other steps. Here’s a sample of what’s required.

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3. Mathematics

Machine Learning is a house built on Math bricks. Browse through the most common components, and send your feedback if you see something missing.

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4. Concepts

A partial list of the types, categories, approaches, libraries, and methodology.

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5. Models

A sampling of the most popular models. Send your comments to add more.

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References

I’m planning to build a more complete list of references in the future. For now, these are some of the sources I’ve used to create this Mindmap.

 Stanford and Oxford Lectures. CS20SI, CS224d.
> Books: 
  > Deep Learning - Goodfellow. 
  > Pattern Recognition and Machine Learning - Bishop. 
  > The Elements of Statistical Learning - Hastie.
- Colah's Blog. http://colah.github.io
- Kaggle Notebooks.
- Tensorflow Documentation pages.
- Google Cloud Data Engineer certification materials.
- Multiple Wikipedia articles.