Advanced Predictive Analytics Techniques
( 25 Modules )

Module #1
Introduction to Advanced Predictive Analytics
Overview of predictive analytics, course objectives, and prerequisites

Module #2
Linear Regression Refresher
Review of linear regression, assumptions, and model evaluation metrics

Module #3
Generalized Linear Models (GLMs)
Introduction to GLMs, logistic regression, and Poisson regression

Module #4
Decision Trees and Random Forests
Introduction to decision trees, random forests, and ensemble learning

Module #5
Gradient Boosting Machines
Introduction to gradient boosting, XGBoost, and LightGBM

Module #6
Support Vector Machines (SVMs)
Introduction to SVMs, kernel methods, and model tuning

Module #7
Clustering Algorithms
Introduction to k-means, hierarchical clustering, and density-based clustering

Module #8
Dimensionality Reduction Techniques
Introduction to PCA, t-SNE, and manifold learning

Module #9
Time Series Forecasting
Introduction to ARIMA, Prophet, and LSTM models

Module #10
Text Analytics and NLP
Introduction to text preprocessing, sentiment analysis, and topic modeling

Module #11
Recommendation Systems
Introduction to collaborative filtering, content-based filtering, and hybrid models

Module #12
Deep Learning for Predictive Analytics
Introduction to neural networks, CNNs, and RNNs for predictive modeling

Module #13
Convolutional Neural Networks (CNNs) for Image Recognition
In-depth look at CNNs for image classification and object detection

Module #14
Recurrent Neural Networks (RNNs) for Sequence Data
In-depth look at RNNs for time series forecasting, language modeling, and text classification

Module #15
Transfer Learning and Domain Adaptation
Introduction to transfer learning, domain adaptation, and few-shot learning

Module #16
Ensemble Methods and Model Stacking
Introduction to ensemble methods, model stacking, and super learning

Module #17
Model Interpretability and Explainability
Introduction to model interpretability techniques, LIME, and SHAP values

Module #18
Hyperparameter Tuning and Model Selection
Introduction to hyperparameter tuning, cross-validation, and model selection methods

Module #19
Big Data Analytics and Spark
Introduction to big data analytics, Apache Spark, and distributed computing

Module #20
Predictive Modeling with Python
Hands-on predictive modeling with Python, scikit-learn, and TensorFlow

Module #21
Predictive Modeling with R
Hands-on predictive modeling with R, caret, and dplyr

Module #22
Case Studies in Advanced Predictive Analytics
Real-world case studies in predictive analytics, including customer churn, fraud detection, and demand forecasting

Module #23
Advanced Topics in Predictive Analytics
Discussion of advanced topics, including autoencoders, GANs, and reinforcement learning

Module #24
Ethics and Fairness in Predictive Analytics
Importance of ethics and fairness in predictive analytics, bias detection, and mitigation strategies

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Advanced Predictive Analytics Techniques career

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Advanced Predictive Analytics Techniques ( 25 Modules )

Advanced Predictive Analytics Techniques
( 25 Modules )