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ai_primer
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AI primer - short course on ML/AI basics
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README Preview
# Welcome to AI primer course
[INTERACTIVE BOOK LINK](https://trokas.github.io/ai_primer/README.html)
Main aim of this course is to give you enough information so that you can start exploring field of AI on your own and maybe even start searching for DS role.
We have only 5 main chapters and one bonus lecture to cover.
- Unsupervised learning
- SVD (Singular Value Decomposition) - it’s a good tool to introduce both technical tools we will be working with as well as giving us a glimpse at unsupervised learning.
- Supervised learning
- RF (Random Forests) - one of the first “silver bullets” out there. Our discussion will also cover Shannon’s work on entropy as it’s one of the key ingredients.
- Deep learning
- DNN (Deep Neural Networks) - we will build our own Perceptron from scratch, thus focusing on gradient descent and backprop on the way. By changing activation function logistic regression will be introduced and finally we will explore what a stack of layers (deep NN) 
