ML Notebooks

Contains code examples for some of machine learning algorithms.

The notebooks are designed to be simple, practical, and easily extensible. You can use them for learning and study without restriction.

Getting Started

Name	Description	Notebook
Simple Linear Regression	A basic implementation of Linear Regression
Multiple Linear Regression	The use of two or more independant variables to predict a dependant variable
Polynomial Regression for Non-Linear modeling	How to model a non-linear relationship between the independent and dependent variables ?
Decision Tree Regression	Splitting data by asking questions, yes those are decision trees.
Logistic Regression	"The probabilty of the occurance of an event, an implementation of logistic regression
K-NN Regression	k-nearest neighbors algorithm (it can be applied to both classification and regression problems).
Pytroch Tutorial Example	Learn how to use the PyTorch machine learning framework.

If you find any bugs or have any questions regarding these notebooks, please open an issue. We will address it as soon as we can.

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
data		data
.gitignore		.gitignore
Decision_Tree_Regression.ipynb		Decision_Tree_Regression.ipynb
KNN_regression.ipynb		KNN_regression.ipynb
LICENSE		LICENSE
LinearRegression_example.ipynb		LinearRegression_example.ipynb
LogisticRegression.ipynb		LogisticRegression.ipynb
MultipleLinearRegression_example.ipynb		MultipleLinearRegression_example.ipynb
Polynomial_Regression_for_Non-Linear_Modelling.ipynb		Polynomial_Regression_for_Non-Linear_Modelling.ipynb
README.md		README.md
logisticRegression_.ipynb		logisticRegression_.ipynb
pytorch_tutorial.ipynb		pytorch_tutorial.ipynb