Master Data Mining in Data Science & Machine Learning
Learn about Data Mining Standard Processes, Survival Analysis, Clustering Analysis, Various algorithms and much more.
Language : english
Note: ?? / 5.0
If you are looking to build strong foundations and understand advanced Data Mining techniques using Industry-standard Machine Learning models and algorithms then this is the perfect course is for you. We have covered everything you need about Data Mining and its processes, Machine Learning Models, and how to implement them in the real world.
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 searching large datasets for patterns humans might not spot.
In 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.
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.
Introduction to Survival Analysis.
Kaplan Meyer Estimator introduction.
Log Rank Test introduction.
Cox Hazards Regression.
Gaussian Mixture Model.
Introduction to Data Reduction.
PCA – Principal Component Analysis.
LDA – Linear Discriminant Analysis.
Association Rule Learning.
Aprior Algorithm and Visualization.
Tree based models.
Attribute selection method- Gini Index and Entropy.
Concept of Bagging.
Introduction to Adaboost and Gradient Boosting.
Introduction to XGBoost.
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.