README.md

Master-Data-Mining-in-Data-Science-Machine-Learning

Data mining means mining the data. It is defined as finding hidden insights(information) from the database and extract patterns from the data.Data mining is an automated process that consists of searcIn this course, you will get advanced knowledge on Data Mining.

This course begins by providing you the complete knowledge about the introduction of Data Mining.

This course is a complete package for everyone wanting to pursue a career in data mining.

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In this course, you will cover the following topics:-

• Data Mining Standard Processes.

  • KDD- Knowledge Discovery in Databases.

  • Introduction to SEMMA.

  • Introduction to CRISP- DM.

  • Introduction to TDSP- Team Data Science Process.

• Survival Analysis.

  • Introduction to Survival Analysis.

  • Kaplan Meyer Estimator introduction.

  • Log Rank Test introduction.

• Cox Hazards Regression.

• Clustering Analysis.

  • KMeans clustering.

  • Gaussian Mixture Model.

• Dimensionality reduction.

  • Introduction to Data Reduction.

  • PCA - Principal Component Analysis.

  • T-SNE.

  • LDA - Linear Discriminant Analysis.

• Association Rule Learning.

  • Transaction List.

  • Encoding Transactions.

  • Aprior Algorithm and Visualization.

• Tree based models.

  • Decision Trees.

  • Attribute selection method- Gini Index and Entropy.

  • Concept of Bagging.

  • Random Forest.

• Boosting Algorithm.

  • Introduction to Adaboost and Gradient Boosting.

  • Introduction to XGBoost.

• Model Explanationability.

  • Introduction to SHAP.

  • Local and Global Interpretability.

  • Introduction to LIME.