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

## What is Occam's Razor?

**Occam's Razor** is credited to the English philosopher William of Ockham. It states that out of two or more competing theories, the simpler theory is to be preferred. 

In this lesson, we’ll see this theory from the lens of machine learning. Occam's Razor is intuitive and is easily understood when applied to real-life concepts. Similarly, we can say that a simple model should be preferred in machine learning. 
 
## What is a simple model?

A machine learning model is a parametric function, and the number of parameters is a quantification of its complexity. The number of parameters is directly proportional to the complexity of the model.

For example, let's take a task $T$ for which we have two models $M_1$ and $M_2$. This is shown in the figure below:


# What is Occam's Razor?

**Occam's Razor** is credited to the English philosopher William of Ockham. It states that out of two or more competing theories, the simpler theory is to be preferred. 

In this lesson, we’ll see this theory from the lens of machine learning. Occam's Razor is intuitive and is easily understood when applied to real-life concepts. Similarly, we can say that a simple model should be preferred in machine learning. 
 
# What is a simple model?

A machine learning model is a parametric function, and the number of parameters is a quantification of its complexity. The number of parameters is directly proportional to the complexity of the model.

For example, let's take a task $T$ for which we have two models $M_1$ and $M_2$. This is shown in the figure below:


Get introduced to Occam’s Razor and compare bias-variance trade-off.

Occam’s Razor

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

Occam’s Razor

What is Occam’s Razor?

What is a simple model?