Introduction to AI in Predictive Analytics
( 25 Modules )

Module #1
Introduction to AI and Predictive Analytics
Overview of AI, predictive analytics, and their applications

Module #2
History and Evolution of AI
From rule-based systems to machine learning and deep learning

Module #3
Types of AI:Narrow, General, and Superintelligence
Understanding the different types of AI and their implications

Module #4
Introduction to Predictive Analytics
Defining predictive analytics, types, and importance

Module #5
Descriptive, Inferential, and Predictive Analytics
Understanding the differences and relationships between the three

Module #6
Data Preprocessing for Predictive Analytics
Importance of data quality, cleaning, and transformation

Module #7
Introduction to Machine Learning
Supervised, unsupervised, and reinforcement learning

Module #8
Regression Analysis
Simple and multiple regression, assumptions, and diagnostics

Module #9
Decision Trees and Random Forests
Introduction to tree-based models and ensemble methods

Module #10
Naive Bayes and K-Nearest Neighbors
Classification algorithms and their applications

Module #11
Support Vector Machines (SVMs)
Introduction to SVMs and their applications

Module #12
Clustering and Dimensionality Reduction
K-means, hierarchical clustering, PCA, and t-SNE

Module #13
Neural Networks and Deep Learning
Introduction to neural networks, perceptrons, and deep learning

Module #14
Convolutional Neural Networks (CNNs)
Image recognition and computer vision

Module #15
Recurrent Neural Networks (RNNs) and LSTMs
Sequence data and natural language processing

Module #16
Model Evaluation and Selection
Metrics, cross-validation, and model tuning

Module #17
Ensemble Methods and Model Stacking
Combining models for improved performance

Module #18
AI in Predictive Analytics:Applications and Case Studies
Real-world examples and applications of AI in predictive analytics

Module #19
AI for Healthcare and Biomedical Applications
Using AI for disease diagnosis, treatment, and patient outcomes

Module #20
AI for Finance and Banking
Using AI for risk analysis, fraud detection, and portfolio optimization

Module #21
AI for Marketing and Customer Analytics
Using AI for customer segmentation, churn prediction, and personalized marketing

Module #22
AI for Supply Chain and Operations Management
Using AI for demand forecasting, inventory optimization, and logistics

Module #23
AI Ethics, Bias, and Fairness
Understanding and addressing ethical concerns in AI development

Module #24
AI Regulations and Governance
Overview of AI-related regulations and guidelines

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Introduction to AI in Predictive Analytics career

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