Series of notebooks that walks through the fundamentals of machine learning and data science.
Important Algorithms for a beginner to learn and implement Machine Learning:
Linear Regression Logistic Regression K-Nearest Neighbours K-Means Clustering Naive Bayes SVMs Decision Trees Random Forest Dimensionality Reduction Algorithms Gradient Boosting algorithms- XGBoost, GBM, LightGBM, CatBoost
Ensure the bug was not already reported by searching on GitHub under issues. If you're unable to find an open issue addressing the bug, open a new issue.
Write detailed information. Detailed information is very helpful to understand an issue.
Pull Requests are always welcome.
MIT © SRM-ACM-Women
This project is licensed under the MIT License
