| Chapter 1: Introduction
Introduction to ML, classification, regression and clusturing, models validation, training, gradient descent
|Chapter 2: Regression
Simple and multiple linear regression, qudratic regression, logistic regression
|Chapter 3: Artificial Neural Network
Perceptron, Multilayer Perceptron, ADALINE, Recurrent neural network, training, activation function, Overfitting, etc.
|Chapter 4: Deep Learning
CNN, RNN, LSTM