Predictive Modeling and Data Mining
( 25 Modules )

Module #1
Introduction to Predictive Modeling and Data Mining
Overview of predictive modeling and data mining, importance, and applications

Module #2
Data Mining Concepts
Data mining definitions, data types, and data quality issues

Module #3
Predictive Modeling Concepts
Predictive modeling definitions, types, and evaluation metrics

Module #4
Data Preprocessing
Data cleaning, data transformation, and data reduction techniques

Module #5
Feature Selection and Engineering
Feature selection methods, feature engineering, and dimensionality reduction

Module #6
Supervised Learning
Introduction to supervised learning, types, and common algorithms

Module #7
Linear Regression
Simple and multiple linear regression, assumptions, and model evaluation

Module #8
Logistic Regression
Binary and multinomial logistic regression, model building, and evaluation

Module #9
Decision Trees
Introduction to decision trees, building, and interpreting decision trees

Module #10
Random Forest
Introduction to random forest, advantages, and applications

Module #11
Unsupervised Learning
Introduction to unsupervised learning, types, and common algorithms

Module #12
K-Means Clustering
K-means clustering algorithm, applications, and model evaluation

Module #13
Hierarchical Clustering
Hierarchical clustering algorithm, dendrograms, and applications

Module #14
Association Rule Mining
Introduction to association rule mining, apriori algorithm, and applications

Module #15
Text Mining
Introduction to text mining, text preprocessing, and text analysis

Module #16
Model Evaluation and Selection
Model evaluation metrics, model selection, and hyperparameter tuning

Module #17
Handling Imbalanced Data
Introduction to imbalanced data, techniques for handling imbalanced data

Module #18
Ensemble Methods
Introduction to ensemble methods, bagging, boosting, and stacking

Module #19
Gradient Boosting
Introduction to gradient boosting, XGBoost, and LightGBM

Module #20
Neural Networks
Introduction to neural networks, architecture, and applications

Module #21
Deep Learning
Introduction to deep learning, convolutional neural networks, and recurrent neural networks

Module #22
Big Data and Scalability
Handling big data, scalability, and distributed computing

Module #23
Data Mining and Predictive Modeling with Python
Hands-on practice with Python libraries for data mining and predictive modeling

Module #24
Case Studies and Applications
Real-world case studies and applications of predictive modeling and data mining

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Predictive Modeling and Data Mining career

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Predictive Modeling and Data Mining ( 25 Modules )

Predictive Modeling and Data Mining
( 25 Modules )