My exercise for Coursera Machine Learning Course.
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Updated
Feb 9, 2017 - MATLAB
My exercise for Coursera Machine Learning Course.
Assignments for Stanford's Machine Learning course on Coursera
Eestech challenge 2017
A simple neural network for classifying handwritten digits
Train a Logistic Regression model using Gradient Descent or Newton's Method
Bernoulli Naive Bayes algorithm
K-Nearest Neighbors Classifier
Implement a linear regression algorithm and use it with a real world dataset. The dataset contains a collection of real estate listings in San Luis Obispo county.
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
Implement a Minimum Risk Bayes Decision Theoretic classifier and use it to classify the test examples in the provided datasets of Iris flowers.
ML classifier for identifying encrypted streaming video traffic.
In this assignment, we are asked to perform a supervised learning task within the framework of computer vision.
Image super-resolution using matrix valued operations
All the graded assignments of the course
EC503Project Fall2017 Anomaly Detection
Implementation of decision tree from scratch
Programming Assignments and Lectures for Andrew Ng's "Machine Learning" Coursera course
Deep Learning using Neural Network Toolbox + Finance Portfolio Selection with MorningStar
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