IPYNBs of my academic course on ML. Here is a description of what each file contains.
#CLASSIFICATION.ipynb
Classification on wisconsin cancer dataset using Logistic Regression
#Cancer_Classification.ipynb
Classification on wisconsin cancer dataset using Logistic Regression, with visualization of PRC and ROC curves
#DecisionTree.ipynb
Decision tree Regression on the melbourne house pricing dataset
Decision tree Classifier on IRIS dataset with PCA
#KMeans.ipynb
Kmeans clustering on the MallCustomers dataset(csv file in repository)
#NAIVE BAYES.ipynb
Naive bayes classification on IRIS dataset
#Neural Net.ipynb
Backprop neural network on the Mnist dataset
#Numpy.ipynb
Solved numpy exercise from a course i was pursuing(for a quick reference)
#PCA.ipynb
Implemented PCA on the IRIS dataset
#Pre-processing.ipynb
Preprocessing on the train dataset implementing Linear Regression
#SVM-checkpoint.ipynb
SVM classifier on IRIS datset with visualized decision boundaries
#SVM.ipynb
A simple SVM classifier on the IRIS datset
#python_tut_AI_circle.ipynb
A quick reference notebook for basic python