Machine Learning

A computer program is said to learn from experience (E) with respect to some task (T) and some performance (P), if its performance on ‘T’, as measured by ‘P’, improves with experience ‘E’.

Supervised Learning

Unsupervised Learning

Regression

Logistic Regression

Linear Regression

$y = w*x + b$

Loss Function

Calculates the “goodness” of a function.

$L(f) = \sum_{n=1}^n (\hat{y} - f(x^n))^2 \rarr Estimation of error$

Where $f(x^n)$ is the loss function, i.e. $y = w*x + b$

Gradient Descent

Model Selection

Bias and Variance

Cross Validation

Adaptive Learning Rate

Stochastic Gradient Decent

Feature Scaling

Classification

Multi-class Classification

Deep Learning