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

# Multiclass extension
The logistic regression model offers two significant advantages: the ability to learn probabilities and the natural extension to handle multiple classes. Let's explore how we can extend a binary classification model to a multiclassification model with $c$ classes. This technique is also called one-vs-all (one-vs-rest).

## Algorithm
For each class $j$ in the dataset:

1. Set the labels of class $j$ to 1, indicating positive instances.
2. Set the labels of all other classes to 0, representing negative instances.
3. Apply logistic regression on the modified dataset, treating it as a binary classification problem with class $j$ as the positive class (1) and all other classes as the negative class (0). Save the corresponding model parameters $\boldsymbol{\bold w_j}$.

4. Predict the probability $\hat{y}_{tj} = \sigma(\bold w^T_j\phi(\bold x_t))$, where $\bold{x}_t$ is the input vector of the test instance.

5. Rescale all class predictions $\hat{y}_{tj}$ by dividing each by the sum of all predictions across all classes $\sum_{j=1}^c \hat{y}_{tj}$. This step ensures that the probabilities sum up to 1, providing normalized probabilities.

6. Finally, select the class with the maximum rescaled prediction as the predicted class for the test instance $\bold{x}_t$.




Explore the multiclass extension through softmax activation.

Multiclass Formulation

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

Multiclass Formulation

Multiclass extension

Algorithm