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

# Kernels in SVM
The dual formulation straightforwardly offers kernelization of SVM. As we notice in the following dual optimization problem, the Gram matrix $K$ can be computed using any kernel function:
$$
\begin{aligned}
\max_{\bold a} \quad & \bold a^T\bold 1 - \frac{1}{2}\bold a^T_{\bold y}K\bold a_{\bold y}\\
\textrm{s.t.} \quad & 0\le \bold a \le C
\end{aligned}
$$

The prediction $\bold a^T_{\bold y}\Phi(X)^T\phi(\bold x_t)$ can also be made using the same kernel function in place of $\Phi(X)^T\phi(\bold x_t)$.

# Implementation
The following code implements a binary classification SVM using various kernel functions (linear, polynomial, and RBF) on a synthetic dataset. It splits the data into training and test sets, fits the SVM using the training set, and evaluates the accuracy on the test set. The SVM optimization problem is formulated using `cvxpy` and solved using a convex optimization solver. Additionally, the code generates a decision boundary plot for visualization.


Learn how to implement kernel SVM and observe sparsity in the solution vector for better generalization.

Kernel SVM and Sparsity

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

Kernel SVM and Sparsity

Kernels in SVM