Gain insights into machine learning concepts, implement and compare algorithms with scikit-learn, and explore supervised learning, clustering, regression, SVMs, autoencoders, and ensemble learning with practical projects using Python.

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

# Logistic Regression
**Logistic regression** is a discriminative model widely used for classification tasks. The term "logistic" in logistic regression refers to the utilization of the logistic function. 
Consider a binary classification problem: the target variable $y_i$ can take on values in $\{0, 1\}$. One way to model the probability distribution of the target label $y_i$ being equal to 1 given the feature vector $\phi(\bold{x}_i)$ is by employing a logistic function defined as:

$$\hat{y}_i=p(y_i=1|\phi(\bold{x}_i))=\frac{1}{1+e^{-\bold w^T \phi(\bold{x}_i)}}$$

The complementary probability $p(y_i=0|\phi(\bold x_i))$ can be obtained as:

$$
p(y_i=0|\phi(\bold{x}_i))= 1-p(y_i=1|\phi(\bold{x}_i))
$$

## Sigmoid function
A logistic function is also known as **sigmoid**, typically denoted by  $\sigma(z)$. 
$$\sigma(z)=\frac{1}{1+e^{-z}}$$



Learn about the sigmoid function, logistic regression, and its optimization via BCE loss.

Introduction to Logistic Regression

Learn about the sigmoid function, logistic regression, and its optimization via BCE loss.

Course Overview

Supervised 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

Introduction to Logistic Regression

Logistic Regression

Sigmoid function