100 Days of ML Coding

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

Collection of notebooks about quantitative finance, with interactive python code.

Code for Tensorflow Machine Learning Cookbook

Python code for common Machine Learning Algorithms

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

Plain python implementations of basic machine learning algorithms

Bare bone examples of machine learning in TensorFlow

Powerful modern math library for PHP: Features descriptive statistics and regressions; Continuous and discrete probability distributions; Linear algebra with matrices and vectors, Numerical analysis; special mathematical functions; Algebra

For extensive instructor led learning

Large-scale linear classification, regression and ranking in Python

🙄 Difficult algorithm, Simple code.

General Assembly's 2015 Data Science course in Washington, DC

Bare bones introduction to machine learning from linear regression to convolutional neural networks using Theano.

🤖 MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics being explained

gesture recognition toolkit

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

Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

Example demonstrating how gradient descent may be used to solve a linear regression problem

Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

Compendium of free ML reading resources

Fast Best-Subset Selection Library

Supervised Descent Method Apply to Face Alignment, and Head Pose Estimation with Linear Regression. It is cross-platfrom, easily compile in windows, ubuntu, even in Android & iOS.

Multivariable regression library in Go

The official code repository supporting the book, Grokking Artificial Intelligence Algorithms

The full collection of Jupyter Notebook labs from Andrew Ng's Machine Learning Specialization.

Regression, Scrapers, and Visualization

Predicting house prices using Linear Regression and GBR

A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)