Popular GitHub Repositories for Linear Regression

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

Bare bone examples of machine learning in TensorFlow

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

Compendium of free ML reading resources

Simple machine learning library / 簡單易用的機器學習套件

Lesson material on data science and machine learning topics/concepts

Examining how large language models (LLMs) perform across various synthetic regression tasks when given (input, output) examples in their context, without any parameter update

A library for machine learning that builds predictions using a linear regression.

SecMML (Queqiao): Secure MPC (multi-party computation) Machine Learning Framework.

Collection of various implementations and Codes in Machine Learning, Deep Learning and Computer Vision ??

Stock price trend prediction with news sentiment analysis using deep learning

This repository contains all the programming exercises in Python for the Coursera course called "Machine Learning" by Adjunct Professor Andrew Ng at Stanford University.

Machine Learning Procedures and Functions for Neo4j

Dynamic movement primitives (DMPs) are a method of trajectory control/planning from Stefan Schaal’s lab. Complex movements have long been thought to be composed of sets of primitive action ‘building blocks’ executed in sequence and \ or in parallel, and DMPs are a proposed mathematical formalization of these primitives. The difference between DMPs and previously proposed building blocks is that each DMP is a nonlinear dynamical system. The basic idea is that you take a dynamical system with well specified, stable behavior and add another term that makes it follow some interesting trajectory as it goes about its business. The DMP differential equations (Transformation System, Canonical System, Non-linear Function) realize a general way of generating point-to-point movements. Imitation learning using linear regression is performed to com

Use Python and NLTK to build out your own text classifiers and solve common NLP problems

Intro to Deep Learning by National Research University Higher School of Economics

NTUEE Machine Learning, 2017 Spring

I have implemented Recurrent Neural Network (RNN model) to predict the future stock prices and compare it with linear regression.

# Machine Learning (Coursera) This is my solution to all the programming assignments and quizzes of Machine-Learning (Coursera) taught by Andrew Ng. After completing this course you will get a broad idea of Machine learning algorithms. Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code. ## Contents * Lectures Slides * Solution to programming assignment * Solution to Quizzes by Andrew Ng, Stanford University, [Coursera](https://www.coursera.org/learn/machine-learning/home/welcome) ### Week 1 - [X] Videos: Introduction - [X] Quiz: Introduction - [X] Videos: Linear Regression with One Variable - [X] Quiz: Linear Regression with One Variable ### Week 2 - [X] Videos: Linear Regression with Multiple Variables - [X] Quiz: Linear Regression with Multiple Variables - [X] Videos

I have had trouble in the past finding a place where I could learn about statistical learning algorithms, resources as to how to learn them and the code associated with it. This is my attempt at remedying that issue.