| Content | |
|---|---|
| Chapter 1: Introduction Introduction to ML, classification, regression and clusturing, models validation, training, gradient descent |
01 |
| Chapter 2: Regression
Simple and multiple linear regression, qudratic regression, logistic regression |
02 |
| Chapter 3: Artificial Neural Network
Perceptron, Multilayer Perceptron, ADALINE, Recurrent neural network, training, activation function, Overfitting, etc. |
03 |
| Chapter 4: Deep Learning
CNN, RNN, LSTM |
04 |