Machine Learning from Scratch

Last update: Jul 05, 2022

Overview

Machine Learning from Scratch

Author: Shengxuan Wang

From: Oregon State University

Content:

Building Machine Learning model from Scratch, without using any ML package. Only use data processing package, e.g. Pandas and Numpy, and a package I made: Popanda (see the detail here: https://github.com/shawn120/Popanda_Enhance_of_Pandas) Then using some data set to test them.

Models include Linear Regression, Logistic Regression, Perceptron, and Decision Tree.

Owner

ShawnWang

GitHub Repository

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Machine Learning from Scratch

Related tags

Overview

Machine Learning from Scratch

Content:

Owner

ShawnWang

STUMPY is a powerful and scalable Python library for computing a Matrix Profile, which can be used for a variety of time series data mining tasks

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Machine Learning from Scratch

Related tags

Overview

Machine Learning from Scratch

Content:

Owner

ShawnWang

STUMPY is a powerful and scalable Python library for computing a Matrix Profile, which can be used for a variety of time series data mining tasks

﻿Greykite: A flexible, intuitive and fast forecasting library

Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

AutoX是一个高效的自动化机器学习工具，它主要针对于表格类型的数据挖掘竞赛。 它的特点包括: 效果出色、简单易用、通用、自动化、灵活。

FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

End to End toy example of MLOps

Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.

distfit - Probability density fitting

ThunderSVM: A Fast SVM Library on GPUs and CPUs

Visualize classified time series data with interactive Sankey plots in Google Earth Engine

This jupyter notebook project was completed by me and my friend using the dataset from Kaggle

Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python

Random Forest Classification for Neural Subtypes

Retrieve annotated intron sequences and classify them as minor (U12-type) or major (U2-type)

scikit-learn: machine learning in Python

A collection of Machine Learning Models To Web Api which are built on open source technologies/frameworks like Django, Flask.

AtsPy: Automated Time Series Models in Python (by @firmai)

Backtesting an algorithmic trading strategy using Machine Learning and Sentiment Analysis.

Solve automatic numerical differentiation problems in one or more variables.

Greykite: A flexible, intuitive and fast forecasting library

AutoX是一个高效的自动化机器学习工具，它主要针对于表格类型的数据挖掘竞赛。它的特点包括: 效果出色、简单易用、通用、自动化、灵活。