Master Data Mining in Data Science & Machine Learning
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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.
- Survival Analysis.
- Introduction to Survival Analysis.
Kaplan Meyer Estimator introduction.
Log Rank Test introduction.
- Introduction to Survival Analysis.
- 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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Introduction to Clustering -
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Types of Clustering
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25
Applications of Clustering
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26
Using the Elbow Method for Choosing the Best Value for K
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27
Introduction to K Means Clustering
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Solving a Real World Problem
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29
Implementing K Means on the Mall Dataset
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30
Using Silhouette Score to analyze the clusters
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31
Clustering Multiple Dimensions
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32
Introduction to Hierarchal Clustering
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Introduction to Dendrograms
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34
Implementing Hierarchical Clustering
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35
Introduction to DBSCAN Clustering
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36
Implementing DBSCAN Clustering
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37
Why High Dimensional Datasets are a Problem
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38
Methods to solve the problem of High Dimensionality
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39
Solving a Real World Problem
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40
Introduction to Correlation using Heatmap
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41
Removing Highly Correlated Columns using Correlation
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42
Introduction to Variance Inflation Filtering
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43
Implementing VIF using statsmodel
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44
Introduction to Recursive Feature Selection
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45
Implementing Recursive Feature Selection
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46
Introduction the Boruta Algorithm
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47
Implementing the Boruta Algorithm
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48
Introduction to Principal Component Analysis
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49
Implementing PCA
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50
Introduction to t-SNE
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51
Implementing t-SNE
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52
Introduction to Linear Discriminant Analysis
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53
Implementing LDA
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54
Difference between PCA, t-SNE, and LDA
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61
Intuition for decision trees
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Attribute selection method- Gini Index and Entropy
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63
Advantages and Issues with Decision trees
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Implementing Decision tree using Sklearn
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Understanding the concept of Bagging
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66
Introduction to Random forest
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Understanding the parameters of Random forest
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Implementing random forest using Sklearn
