Master Data Mining in Data Science & Machine Learning

Master Data Mining in Data Science & Machine Learning, Learn about Data Mining Standard Processes, Survival Analysis, Clustering Analysis, Various algorithms and much more.

• • s.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.

This course is a complete package.

Lots and lots of quizzes and exercises are waiting for you.

You will also have access to all the resources used in this course.

Enroll now and become an expert in Data Mining.