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

The Stanford Coursera course on MachineLearning with Andrew Ng

This repo is for anyone who wants help in understanding and solving Andrew Ng's Coursera Course on Machine Learning Assignments and Quizes. (YEAR 2020)

Octave code of Stanford Machine Learning in Coursera (all passed).

CS229, Spring 2015

Programming assignments for Machine Learning by Standford University

THe course in taken in March, 2016 given by stanford on Coursera named Machine Learning.

Exercise codes for Stanford Machine Learning course on Coursera

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Course Exercise

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coursera machine learning course exercices

This repository contains my project activities accomplished during "Stanford-Machine Learning" Course . This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills focusing on advanced learning algorithms and reinforcedd learning

This course provides a broad introduction to machine learning and statistical pattern recognition. Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. Explore recent applications of machine learning and design and develop algorithms for machines.

Musculoskeletal Radiographs (MURA) dataset, proposed by Stamford MachineLearning (ML) group, contains 40,561 images of bone X-rays from 14,863 studies.The X-ray images belong to seven body areas of upper extremity- Wrist, Elbow,Finger, Humerus, Forearm, Hand, and Shoulder. The data are classified manually byradiologists into two classes- normal or abnormal. These data samples are labeledusing majority vote by six board-certified Stanford radiologists. The majority votesof these radiologists’ labels are considered as gold standard. The presence of suchrich,complex and diverse labeled dataset inspires to build an accurate but simplermodel for bone anomaly detection. The model proposed by Stamford ML group isa 169 layer deep computationally complex Neural Network (NN), that requires aGraphical Processing Unit (GPU) for implementation. This leads to the necessityof smaller neural network based model that are executable on general purposecomputers. Moreover, the 169 layer deep model works well on par with the goldstandard except for the humerus radiographs, despite the presence of humerusdata labeled with high accuracy. Therefore, in this work we propose an ensembleof smaller neural networks and convolution neural network for highly accurateclassification of MURA study images of humerus. We use Adaboost algorithmto train this model. The performance of this model is evaluated using trainingerror, validation error, and Cohen’s kappa coefficients. The model is available athttps://github.com/mythgotham007/Mura_Humerus_CNN-NN/import

This repository is created for interview and makes sure they could easily check what I have done

Machine Learning on Coursera: https://www.coursera.org/learn/machine-learning

Coursera_MachineLearning_Stanford

Solved Exercises for Stanford University - Machine Learning

Completed assignments for the Stanford ML course taught on Coursera

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Exercise and notes for Machine Learning course of Stanford.

All results of my exercises and projects of Stanford University's Machine Learning online course. (January 2018 - March 2018)

Hello there! This is the culmination of my journey through the Stanford University Machine Learning Specilization, partnered with Deeplearning.AI. :)

Machine Learning course of Stanford.

These are the projects which i have completed under stanford university Machine learning

Solution from 2011 Stanford ML class

Archive of completed exercises from the Stanford Machine Learning course on Coursera (not to be used by participants of the course).

Coursera assignments