Coursera lecture by Andrew Ng : https://www.coursera.org/learn/machine-learning/home/welcome
Here you will find my implementation of the Course Machine Learning from Coursera.
I have implemented this using Python.
All exercises represent different machine learning algorithm such as :
Logistic Regression, Neural Network, Support Vector Machines or Clustering.
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Ex1 : Linear Regression
Implementation of Cost function , Gradient descent, normal equation , normalized features ...
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Ex2 : Logistic Regression
Implementation of a function to plot classification data, a logistic Regression cost function, a logistic Regression Prediction function ...
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Ex3 : Multi-class classification and Neural Network
Implementation of a function to Train a one-vs-all multi-class classifier, Predict using a one-vs-all multi-class classifier, Neural network prediction function, ...
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Ex4 : Neural Networks Learning
Implementation of a function which compute Numerical Gradient, a function to check the difference between the value of gradient and numerical grandient, Neural network prediction function, Neural network cost function, ...
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Ex5 : Regularized Linear Regression and Bias v.s Variance
Implementation of Regularized linear regression cost function, a function to Generates a learning curve, Generates a cross validation curve, ...
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Ex6 : Support Vector Machines/ Application to Spam Classification
Implementation of Gaussian kernel for Support Vector Machine, Email preprocessing, Feature extraction from emails, ...
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Ex7 : K-means Clustering and Principal Component Analysis
Implementation of a function which Projects a data set into a lower dimensional space, Recovers the original data from the projection, Find closest centroids, Compute centroid means,...
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Ex8 : Anomaly Detection and Recommender Systems
Implementation of a function which estimate the parameters of a Gaussian distribution with a diagonal covariance matrix, Find a threshold for anomaly detection, Implement the cost function for collaborative filtering, ...