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

In life, we try to group many things to understand them better or even simplify them. Let's say you and your friend are trying to classify video games. Your friend might want to classify them based on genre and end up with a collection of all the video games in neat little clusters. While classifying video games by their developers, you might have a different collection from your friend's collection. 

&gt; **Note:** Both collections originated from the same data pool (video games), and both have learned something interesting from the data.

In machine learning, we do something similar. We ask the machine to group the data to get meaningful information. This grouping of unlabeled data is called **clustering**. This grouping is based on similarity among data points. After clustering, the data points should be similar within clusters and dissimilar across clusters. 

We can see a lot of data points below:


Explore clustering algorithms and their limitations with sample datasets and similarity functions.

Introduction to Clustering

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 Clustering