Machine Learning Basics

This repository contains implementations of various machine learning algorithms using popular libraries such as Scikit-Learn and TensorFlow. Whether you're a beginner looking to understand basic concepts or an experienced practitioner seeking to explore advanced techniques, this repository has something for everyone.

Table of Contents

• Linear Regression
• Logistic Regression
• Scikit-Learn Basics
• SoftMax Regression
• TensorFlow Softmax Regression
• Tree Ensemble and Decision Trees
• XG Boost

Linear Regression

Implementations of simple linear regression models. Linear regression is a fundamental algorithm used for predicting a continuous outcome.

Logistic Regression

Implementation of logistic regression models, a widely used algorithm for binary classification problems.

Scikit-Learn Basics

Exploration of basic modules provided by Scikit-Learn, a powerful machine learning library in Python. These modules cover preprocessing, model selection, and evaluation.

SoftMax Regression

SoftMax regression is an extension of logistic regression used for multi-class classification problems. This section includes the SoftMax regression implementation.

TensorFlow Softmax Regression

Implementation of Softmax regression using TensorFlow, a popular open-source machine learning library.

Tree Ensemble and Decision Trees

Introduction to decision trees and ensemble methods. This section includes implementations of decision trees and tree ensembles.

XG Boost

Implementation of XG Boost, an efficient and scalable implementation of gradient boosting. XG Boost is known for its speed and performance in various machine learning tasks.