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Coursera Ml Andrewng Notes ⭐ 28,845

吴恩达老师的机器学习课程个人笔记

Shogun ⭐ 2,981

Machine Learning Octave ⭐ 581

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

Machinelearning ⭐ 406

Machine Learning

Matlab Octave ⭐ 250

This repository contains algorithms written in MATLAB/Octave. Developing algorithms in the MATLAB environment empowers you to explore and refine ideas, and enables you test and verify your algorithm.

Keras Gp ⭐ 219

Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.

Ml Course ⭐ 168

Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

Ordinal Regression and Classification Algorithms

Machinelearning ⭐ 65

机器学习师从Andrew Ng(吴恩达)，获得在Coursera平台上斯坦福大学Andrew Ng（吴恩达教授）机器学习（Machine Learning）的资格证书，为了有一个平台和大家分享和交流机器学习，因此特地在此进行课程的：笔记整

Machine Learning Exercise ⭐ 54

Python implementation of the programming assignment from Machine Learning class on Coursera, which is originally implemented in Matlab/Octave.

Py Coursera ⭐ 47

Python framework for Coursera PGM and ML homeworks

Liquidsvm ⭐ 45

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-s

Ml Class ⭐ 39

My solutions to programming exercises from Stanford online Machine Learning class.

The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.

Ml Class ⭐ 26

Lectures, exercises, and assignments for Stanford's ML class, in Scalala

Cs273a Introduction To Machine Learning ⭐ 22

Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions

Machine Learning Coursera ⭐ 15

Stanford Machine Learning MOOC from Coursera by Andrew Ng.

Coursera Machine Learning ⭐ 13

[WIP] In this repository I implemented all assignments in python. (No octave to python library)

Booklibrary ⭐ 12

Book Library of P&W Studio

Octave Convolution Implementation in PyTorch

Sound Classifier ⭐ 9

Deep Learning model to identify common sounds and noises

Movies Recommender ⭐ 8

A system to recommend movies according to ratings provided by users using Collaborative Filtering Learning Algorithm.

Tensorflow.m ⭐ 8

MATLAB/Octave bindings for TensorFlow

Coursera Ml ⭐ 7

💡This repository contains all of the lecture exercises of Machine Learning course by Andrew Ng, Stanford University @ Coursera. All are implemented by myself and in MATLAB/Octave.

Stanford Machine Learning Course On Coursera ⭐ 7

In this repository, you will find the lecture slides and programming assignments that i have done during the course.

Shelf_textbook ⭐ 7

Mes notes de cours et fascicules de TP partagés avec les étudiants du dép. GE de l'ISET de Bizerte.

Traffic Light Detection ⭐ 6

Real time traffic light detection using Machine Learning

Mnist_octave ⭐ 6

Loading MNIST handwritten digits with Octave

Machinelearning Andrew Ng ⭐ 6

吴恩达的机器学习课程的练习（使用OCtave）

Machine Learning By Andrewng Exercises ⭐ 5

The solutions to the exercises done during Machine Learning course by Andrew Ng on Coursera.

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