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

# What is convex optimization?
Convex optimization is a mathematical optimization technique that optimizes problems with convex objective functions and constraints.

## Standard form
The standard form of a convex optimization problem is as follows:
$$
\begin{aligned}
\min_{\bold x} \quad & f_0(\bold x)\\
\textrm{s.t.} \quad & f_i(\bold x)\le0 \quad & i=1,2,\dots,m\\
  \quad & g_j(\bold x)=0 \quad & j=1,2,\dots,k    \\
\end{aligned}
$$

Here, the objective function $f_0$ and the inequality constraints $f_i$ are all convex, and the equality constraints $g_j$ are linear.

# What is a convex function?
A convex function is a real-valued function #key# whose graph lies above the line segment: This means that the function is curving upward and doesn't have any dips or humps that would cause the line segment to dip below the graph. #key# connecting any two points on the graph. This property ensures that the function has a unique global minimum, making it useful in optimization problems. Convex functions are widely used in various fields, including mathematics, economics, physics, and engineering, due to their simplicity and tractability in modeling real-world phenomena. In #concept=cvxML# machine learning, convex functions are commonly used as objective functions #concept# in optimization problems, where the goal is to find the values of parameters that minimize or maximize the function.

Discover the power of convex optimization, including its standard form, important properties, and code examples.

Convex Optimization

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

Convex Optimization

What is convex optimization?

Standard form