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I am looking for a framework or chart that classifies all the different types of data analysis/machine learning. I want to use this classification to organize my knowledge/areas to learn.

Examples of what I mean by types of data analysis/ML include: - Geographical Maps - Image Recognition (CV, OCR...) - Signal Processing - Text/Speech Comprehension

Is there a chart/framework classifying all those domains ?

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You can use ML/DA in almost every sector, but to generalise there are few domains which i would like to list

Broadly it is classified based on data

  1. Text data
  2. Image data
  3. Signal/Periodic Data
  4. Financial data/Numbers

In here we can further classify or give names to the domains. Text data broadly falls under Natural Language Processing (NLP). Here you can work with text generation, text segmentation, document classification, chatbots, topic modelling, Summary extraction & much more NLP Research, NLP Application I have added links to 2 articles listing current research & applications by current industry standards.

Image data is broadly falls under Computer vision. Here you work with videos, live streams, images. They can be satellite images, road accidents, machine parts etc. You can do analysis of football match, or even do analysis on plastic pollution in oceans. Sub topics can be classified as

  1. Image tagging
  2. Image based information gathering
  3. Object prediction
  4. Vision based health prediction
  5. Biological researches
  6. Image rectification using NNs

And much more. If you want to have hands-on experience for image based data, please visit this site, here you will get basic idea of what topics one can achieve with image data.

Periodic data consists of measurements from machines, observations of how they work, it also includes biological data. Basically our normal signal processing or risk analysis. It's application is much needed for machinery makers, health sectors & financial brokers, vehicle positioning etc. Anything which has some time based data into it can be used to simplify routes, analyse risks & avoid unnecessary wear & tear.

But generally this domains are used together with each other unless you go for research. Working for a solution firm i have encountered many projects where i had to gather text data from image & use topic modelling for classification. Had a project where i had to analyse data generated from video cam observing human to predict their HR for next few seconds.

Hope this helps you in identifying what domain interests you more.

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I found this very useful when I was starting my journey in data analysis-

https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-ssl.com/wp-content/uploads/2013/11/Machine-Learning-Algorithms-Mindmap-Sample.png

Here you can find the detailed information-

https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

Hope you find it useful too.

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Starting with the typical supervised vs unsupervised vs reinforcement learning could be more reasonable.

Such a categorisation enables easier search for specific fields where these are used. Just go to Wikipedia and in the articles about them look for their applications (or ,,see also" articles). This way you can build your own chart of "ML types" from top to bottom. This might be a good start https://en.wikipedia.org/wiki/Machine_learning

As you go from the general to the specific, there is a smaller chance that you will miss something just because you do not randomly come up with it.

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