Master Data Mining in Data Science & Machine Learning

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  • Reading time:11 mins read


Learn about Data Mining Standard Processes, Survival Analysis, Clustering Analysis, Various algorithms and much more.

What you will learn

Get started with Data Mining.

Learn about different Data Mining Standard Processes.

Learn the concept of Survival Analysis.

Learn about the concept of Cox Hazards Regression.

Perform Clustering Analysis.

Learn about the Dimensionality reduction.

Learn about the concept of Association Rule Learning.

Learn about the Predictive Modelling.

Learn everything about Data Mining and its applications.

Understand Machine Learning and their connections with Data Mining.

Learn all Machine Learning algorithms,their types and their usage.

Practical use of Data Mining.

Use real world examples of Data Mining.

Description

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:-


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  • 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.

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.

Instructor Support – Quick Instructor Support for any queries.

I’m looking forward to see you in the course!

 

Enroll now and become an expert in Data Mining.

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Content

Introduction
Introduction to Data Mining
Why Data Mining is Necessary
Data Mining Standard Processes
Introduction to KDD
KDD process steps
Pros and Cons of KDD
Introducing SEMMA
Stages of SEMMA
Introduction to CRISP-DM
CRISP-DM Phases
Pros and Cons of CRISP-DM
Introducing TDSP
Pros and Cons of TDSP
Survival Analysis
Introduction to Survival Analysis
Kaplan Meyer Estimator
Censoring
Kaplan Meyer Estimator using Python
Plotting Survival Curves
Log Rank Test Introduction
Log Rank Test using Python
Cox Hazard Regression
Introduction to Cox Hazard Regression
Applying Cox Hazard Regression
Interpreting Results
Clustering Analysis
Introduction to Clustering
Types of Clustering
Applications of Clustering
Using the Elbow Method for Choosing the Best Value for K
Introduction to K Means Clustering
Solving a Real World Problem
Implementing K Means on the Mall Dataset
Using Silhouette Score to analyze the clusters
Clustering Multiple Dimensions
Introduction to Hierarchal Clustering
Introduction to Dendrograms
Implementing Hierarchical Clustering
Introduction to DBSCAN Clustering
Implementing DBSCAN Clustering
Dimensionality Reduction
Why High Dimensional Datasets are a Problem
Methods to solve the problem of High Dimensionality
Solving a Real World Problem
Introduction to Correlation using Heatmap
Removing Highly Correlated Columns using Correlation
Introduction to Variance Inflation Filtering
Implementing VIF using statsmodel
Introduction to Recursive Feature Selection
Implementing Recursive Feature Selection
Introduction the Boruta Algorithm
Implementing the Boruta Algorithm
Introduction to Principal Component Analysis
Implementing PCA
Introduction to t-SNE
Implementing t-SNE
Introduction to Linear Discriminant Analysis
Implementing LDA
Difference between PCA, t-SNE, and LDA
Association Rule Learning
Introduction to Association Analysis
Association Rule Learning
Creating Transaction List
Encoding Transactions
Aprior Algorithm
Aprior Visualisation
Tree Based Models
Intuition for decision trees
Attribute selection method- Gini Index and Entropy
Advantages and Issues with Decision trees
Implementing Decision tree using Sklearn
Understanding the concept of Bagging
Introduction to Random forest
Understanding the parameters of Random forest
Implementing random forest using Sklearn
Introduction to Boosting models
Understanding the concept of boosting
Intuition for Adaboost and Gradient Boosting
Implementing AdaBoost using sklearn
Implementing Gradient Boosting using sklearn
Getting High level intuition for XGBoost
Implementing XGBoost using sklearn
Introduction to Ensembling techniques
Outro Section
Conclusion
How to Get Your Certificate of Completion
Bonus Section
Bonus Lecture
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