Learn machine learning with scikit-learn, covering supervised learning, clustering, regression, SVMs, autoencoders, and ensemble methods through practical Python projects.

ml.tar.gz

Playground

Playground-SPA

Complexity CH-1

regulizer CH-1

regulizer CH-1-S

regulizer ch1_v2

complexity ch1_v2

code_widget

ST_kernel_trick-LIVE

python

pytorch

ST_Autoencoders

code_widget_torchsummary

code_widget_tqdm

SPA_tqdm

AE_multiple

SPA_server

Implementation of NN

c1l3

Implementation of NN-copy

This course focuses on core concepts, algorithms, and machine learning techniques. It explores the fundamentals, implements algorithms from scratch, and compares the results with scikit-learn, the Python machine learning library. This course contains examples, theoretical knowledge, and codes for various ML algorithms.

You’ll start by learning the essentials of machine learning and its applications. Then, you’ll learn about supervised learning, clustering, and constructing a bag of visual words project, followed by generalized linear regression, support vector machines, logistic regression, ensemble learning, and principal component analysis. You’ll also learn about autoencoders and variational autoencoders and end with three exciting projects.

By the end, you’ll have a solid understanding of machine learning and its algorithms, hands-on experience implementing such algorithms and applying them to different problems, and an understanding of how each algorithm works with the provided examples.

Fundamentals of Machine Learning: A Pythonic Introduction

# Parametric model
**Parametric models** are functions defined by a fixed set of parameters in machine learning. These are assumed to be able to approximate the underlying pattern of the data. By adjusting the values of the parameters during the training process, these models can learn to fit the data and make accurate predictions on new inputs.
 Consider this function: $f(x_1,x_2)=2x_1-3x_2+7$ with inputs $x_1$ and $x_2$. This function is an instance of a class of functions of the form $f_{w_1,w_2,w_0}(x_1,x_2)=w_1x_1+w_2x_2+w_0$, where $w_1=2,w_2=-3$, and $w_0=7$. Here, $w_1,w_2$, and $w_0$ are the parameters, also known as weights, and any choice of these parameters results in an instance from the function class. Notice that the function $f(x_1,x_2)=2x_1-3x_2+7$ is also an instance of the following different function class:

$f_{w_1,w_2,w_3,w_4,w_0}(x_1,x_2)=w_1x_1+w_2x_2+w_3x_1^2+w_4x_2^2+w_0$

where $w_1=2,w_2=-3,w_3=0,w_4=0$, and $w_0=7$

## Examples
- $f_{m,c}(x)=mx+c$ is a function class parametrized by $m$ and $c$, and it represents all the lines in 2D.
- $f_{w_1,w_2,w_3,\dots,w_n}(x_1,x_2,x_3,\dots,x_n)=\sum_{i=1}^nw_ix_i$ is a class of functions that represents the weighted average of a set of objects $x_i$. In this case, all the parameters $w_i$ are non-negative and add up to $1$.

> **Note:** In general if $\bold x$ represents the input and $\bold w$ represents the set of parameters, then $f_{\bold w}(\bold x)$ is the representation of the parametric model.

Let's take a simple example of a line in 2D as a parametric model and implement it. Try to visualize different instances of this class by changing parameters `m` and `c` in **line 15** of the code.

Learn about machine learning models' parameters, hyperparameters, and their importance.

Parameters

Course Overview

Supervised Learning

Detect Cyber Intrusion Using Machine Learning

Clustering

Project: Bag of Visual Words

Generalized Linear Regression

Face Recognition Using Kernel Linear Discriminant

Support Vector Machine

Logistic Regression

Ensemble Learning

Early Stage Diabetes Prediction Using Ensemble Learning

Decoding Dimensions: PCA and Autoencoders

Image Reconstruction Using PCA

Image Colorization using Autoencoders

Colorful Face Generation with VAEs

Appendix

Wrapping Up

Parameters

Parametric model