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

# Overview

Welcome to “Fundamentals of Machine Learning: A Pythonic Introduction!” This introductory course is designed to provide a solid foundation in the world of machine learning (ML), a cutting-edge field that’s transforming industries across the globe. Machine learning is the science of making computers learn and adapt without being explicitly programmed, and it plays a crucial role in data analysis, prediction, and decision-making.

In this course, you'll learn the core concepts, algorithms, and techniques that power machine learning. Whether you're a beginner or an intermediate learner, this course offers something valuable. We’ll dive into the fundamentals, implement algorithms from scratch, and compare our results with scikit-learn, the widely used Python library for machine learning.



# Target audience
This course is designed for both beginners and intermediate learners who want to explore the exciting world of machine learning.

**Beginners:** If you're new to the field of machine learning and have a basic knowledge of programming, [linear algebra](https://www.educative.io/courses/linear-algebra-for-data-science-using-python), probability, and statistics, this course is a perfect starting point. We'll take you through the basics step by step, ensuring you grasp the foundational concepts before diving into more advanced topics.

**Intermediate learners:** If you already have some experience in machine learning and meet the prerequisites, you'll also find this course valuable. We not only cover the fundamentals but also provide hands-on experience by implementing algorithms from scratch and comparing their performance with scikit-learn.

Get an overview of the target audience, prerequisites, and motivation for taking this course.

Introduction to the Course

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

How to Predict the Traffic Volume Using Machine Learning

Introduction to the Course