Predictive Analytics with AI
( 25 Modules )

Module #1
Introduction to Predictive Analytics
Overview of predictive analytics, its importance, and applications

Module #2
Foundations of Artificial Intelligence
Introduction to AI, machine learning, and deep learning concepts

Module #3
Predictive Modeling Fundamentals
Basics of predictive modeling, types of models, and evaluation metrics

Module #4
Data Preprocessing for Predictive Analytics
Data cleaning, feature scaling, and feature engineering techniques

Module #5
Introduction to Python for Predictive Analytics
Python basics, NumPy, Pandas, and Matplotlib for data analysis

Module #6
Supervised Learning Algorithms
Linear regression, logistic regression, decision trees, and random forests

Module #7
Unsupervised Learning Algorithms
K-means clustering, hierarchical clustering, and principal component analysis

Module #8
Introduction to Neural Networks
Basic concepts of neural networks, perceptron, and multilayer perceptron

Module #9
Deep Learning for Predictive Analytics
Convolutional neural networks, recurrent neural networks, and long short-term memory networks

Module #10
Natural Language Processing for Predictive Analytics
Text preprocessing, tokenization, and sentiment analysis using NLTK and spaCy

Module #11
Working with Time Series Data
Time series data types, components, and forecasting methods (ARIMA, Prophet)

Module #12
Ensemble Methods and Model Selection
Bagging, boosting, stacking, and model selection techniques

Module #13
Model Evaluation and Hyperparameter Tuning
Metrics for model evaluation, cross-validation, and hyperparameter tuning techniques

Module #14
Predictive Modeling with Big Data
Working with large datasets, distributed computing, and Spark MLlib

Module #15
Real-World Applications of Predictive Analytics
Case studies in finance, healthcare, marketing, and supply chain management

Module #16
Predictive Analytics in Python with Scikit-learn
Using Scikit-learn for predictive modeling, feature selection, and data preprocessing

Module #17
Predictive Analytics with TensorFlow and Keras
Building neural networks with TensorFlow and Keras for predictive modeling

Module #18
Explainable AI and Model Interpretability
Techniques for explaining and interpreting AI models (LIME, SHAP, TreeExplainer)

Module #19
Fairness and Ethics in Predictive Analytics
Bias detection, fairness metrics, and ethical considerations in predictive modeling

Module #20
Predictive Analytics in the Cloud
Deploying predictive models on cloud platforms (AWS, GCP, Azure)

Module #21
Automated Machine Learning
Using AutoML libraries (H2O AutoML, TPOT) for automated model selection and hyperparameter tuning

Module #22
Human-in-the-Loop Predictive Analytics
Collaborative approach to predictive analytics, human-AI collaboration, and AI-assisted decision-making

Module #23
Case Study:Predictive Analytics in Finance
Real-world application of predictive analytics in finance (credit risk assessment, stock market prediction)

Module #24
Case Study:Predictive Analytics in Healthcare
Real-world application of predictive analytics in healthcare (disease diagnosis, patient outcome prediction)

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

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Predictive Analytics with AI ( 25 Modules )

Predictive Analytics with AI
( 25 Modules )