Hey guys I want my next project in the future to have something to do with machine learning so I wanted to start this to get some thoughts where I could start learning some of the algorithms in the future. I am taking a discrete math course and I have taken data a introductory level data structures course so far.
This is a really good free web guide on machine learning, it may help you or give you some ideas:
SentDex on Youtube has some good tutorials for Python on his channel if you are looking more for application.
If you're interested in Machine Learning from an academic perspective I cannot recommend this course enough. It's a free online course offered by Coursera and gives an excellent overview of common Machine Learning algorithms and practices.
https://www.coursera.org/learn/machine-learning/home/welcome
If you're interested in Machine Learning only for a few one-off projects try to use some of the existing APIs. TensorFlow and SKLearn are both great if you like Python!
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No I plan to go fully into it at some point.
Its why I am a computer science major.
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I would start by looking at something like this to get the basic ideas
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When I was studying Data Mining and Machine learning in NZ, we used these lessons produced by the university. They are pretty easy to follow and were a good foray into Data Mining and Machine Learning algorithms. The first Level is pretty trivial but you get into some really cool algorithms if you stick with it. Here is the link:
https://weka.waikato.ac.nz/dataminingwithweka/preview
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Develop a self learning concept using operators to improve the machines intelligence over sampling user interactions daily. Think of an ai, how will the bot interact with a human in a turn based game, after each game will it self analyse the turns and 'improve' the logic e.g making a certain move x turns earlier to initiate an offensive play. Idek just a thought ~
If you're looking for easy accessible data sets to train an algorithm on:
We used ombrometer data to predict river run-off. Both sets should be open to public in most countries and are quite extensive. Try to find them on the homepage of your meteorological service.
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