Python code for common Machine Learning Algorithms
Learning to create Machine Learning Algorithms
My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings 👍
A library for factorization machines and polynomial networks for classification and regression in Python.
Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
Machine Learning Concepts with Concepts
Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
A simple machine learning framework written in Swift 🤖
Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python
pwtools is a Python package for pre- and postprocessing of atomistic calculations, mostly targeted to Quantum Espresso, CPMD, CP2K and LAMMPS. It is almost, but not quite, entirely unlike ASE, with some tools extending numpy/scipy. It has a set of powerful parsers and data types for storing calculation data.
This repository will contain all the stuffs required for beginners in ML and DL do follow and star this repo for regular updates
This project analyzes and visualizes the Used Car Prices from the Automobile dataset in order to predict the most probable car price
This project attempts to construct a missing well log from other available well logs, more specifically an NMR well log from the measured Gamma Ray (GR), Caliper, Resistivity logs and the interpreted porosity from a well.
This project aims to predict the numbers that are published in each day regarding the amount of Coronavirus (COVID-19) cases and deaths.
This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
Selected problems and their solutions from the book on "Machine Intelligence in Design Automation"
Edureka "Data Science with Python" course solutions
Different modeling techniques like multiple linear regression, decision tree, and random forest, etc. will be used for predicting the concrete compressive strength. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy. The best model will be helpful for civil engineers in choosing the approp...
Calculates and returns coefficients for polynomial regression. (composer package)
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