Machine Learning Algorithm Implementations

Creating a logistic regression algorithm without using a library and making cancer classification with this algorithm model (Kaggle Explained)

A follow up page for the session on Machine and Learning and Deep Learning frameworks at GNR 652 course.

Study notes for Elements of Statistical Learning (ESL) book.

A machine repository for kick-starting Machine Learning in no time!

This project utilizes logistic regression to classify numbers 0 and 1 using sign language gestures. It successfully achieves the task of sign language classification, reaching a test accuracy of 93.54%.

In this tutorial we'll bring the TensorFlow 2 Quickstart to Valohai, taking advantage of Valohai versioned experiments, data inputs, outputs and exporting metadata to easily track & compare your models.

A Python code for data analysis and salary predictions using a multiple linear regression model. The code calculates the intercept and coefficients of the model and makes predictions on sample data.

This Python code represents a machine learning project that builds a simple linear regression model using experience and salary data. It plots the data, constructs the regression model, and visualizes the results.

A Basic tutorial for beginners in Data Science. Contains step by step solution on the Titanic Dataset.

Learn Machine Learning with machine learning tutorials for beginners, ml practicals, ml excerices, Machine Learning Projects, Interview Questions

Machine learning case study

딥러닝 with C++ 소스 코드

Algotrading101 article about Sklearn

Supports de la conférence "Machine Learning pour tous avec python" présentée au Breizhcamp 2019

Build a classifier to predict the outcome of Dota 2 games with the Naive Bayes algorithm and results from 102,944 sample games.

Predict diabetes disease using a Logistic Regression with TensorFlow.js

Demonstrating unsupervised clustering using the K Means algorithm and synthetic color data.

The repository contains exercises on Machine Learning algorithms in R, using RStudio. Used to dive into ML, data preprocessing, data visualisation, and data exploration.