#####ai

####maze search algo

####pattern recognition

####fuzzy theory

####genetic algorithm

####artificial life

####ml

###supervised learning

##ensemble

#random forest

#xgboost

##regression(prediction)

#linear regression

##classification

#logistic regression

#decision tree

#SVM

#KNN

#naive bayesian algorithm

###unsupervised learning

##cluster

#K MEANS

###reinforcement learing

####DL

##CNN

##note solving systems of linear equations in Python Central Limit Theorem Basic concepts of statistics (mean, mean, sample mean, variance, standard deviation, etc.) Pandas and numpy basic settings lib explain Overall Task project (example: create array, create data frame, add lines, add columns, compute from file, write to file, rename columns, delete columns, remove lines, subset, convert)

Library Operations project (example: Creating a histogram, creating a histogram of different colors, adding signatures, adding legends, creating legends) python intro : data types , functions, loops things

https://en.wikipedia.org/wiki/Data_preprocessing

remove outlier

fill nan to average or linear or mode value

use chatgpt

indepandent var : dependent var [ex)rock size length weight color : gold or not] more ez explain -> :

x train : y train

x test : y test

https://www.coursera.org/learn/machine-learning-data-analysis

Its written in russian. hope u can read. I learned from duolingo.

https://en.wikipedia.org/wiki/XOR_gate

https://towardsdatascience.com/how-neural-networks-solve-the-xor-problem-59763136bdd7

https://www.youtube.com/watch?v=kNPGXgzxoHw

#install cuda enabled gpu, tensorflow.org/install/gpu

pip install tensorflow-gpu

test of it

import tensorflow as tf print("TensorFlow version:", tf.version)