Machine learning Beginning using python libraries like numpy,scipy,matplotlib,pandas etc.
Linear Regression:
--Model is trained with training data
--Applying linear square fit method to predict the hypothetic function
--Hypothetic function using gradient descent algorithm for minimization of cost function
--Trainning data is taken from:- https://www.kaggle.com/andonians/random-linear-regression/version/2
Polynomial Regression:
--Comparing polynomial regression on data set with linear regression
Classification Algorithms
--K-nearest neighbours (KNN) with implementation
--Support vector machine(SVM) with implementation
--SVM Kernels and analysing the confidence we get from using different kerenls in SVM with OVO and OVR decisionn function
Clustering :
--K-means
--Mean shift
Logistic Regression:
--training the model
--visualising traning data and test data
--checking accuracy of model Decision Tree Regression:
Random Forest Regression: