linear-regression

100 Days of ML Coding

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

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

Plain python implementations of basic machine learning algorithms

Python code for common Machine Learning Algorithms

Bare bone examples of machine learning in TensorFlow

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

For extensive instructor led learning

gesture recognition toolkit

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

Regression, Scrapers, and Visualization

Multivariable regression library in Go

Machine Learning Lectures at the European Space Agency (ESA) in 2018

Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

该存储库包含由deeplearning.ai提供的相关课程的个人的笔记和实现代码。

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

Pure Javascript manually written 👌 implementation of BLAS, Many numerical software applications use BLAS computations, including Armadillo, LAPACK, LINPACK, GNU Octave, Mathematica, MATLAB, NumPy, R, and Julia.

Six snippets of code that made deep learning what it is today.

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

Launch machine learning models into production using flask, docker etc.

A New, Interactive Approach to Learning Python

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

吴恩达机器学习coursera课程，学习代码(2017年秋) The Stanford Coursera course on MachineLearning with Andrew Ng

A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.

Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.