Predictive Analytics Techniques
( 25 Modules )

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

Module #2
Data Preparation for Predictive Analytics
Data collection, cleaning, transformation, and preprocessing for predictive modeling

Module #3
Exploratory Data Analysis (EDA)
Data visualization, summary statistics, and correlation analysis

Module #4
Regression Analysis
Simple and multiple linear regression, assumptions, and interpretation

Module #5
Logistic Regression
Binary classification, logistic function, and model evaluation metrics

Module #6
Decision Trees
Introduction to decision trees, CART algorithm, and tree-based models

Module #7
Random Forest
Ensemble learning, random forest algorithm, and hyperparameter tuning

Module #8
Support Vector Machines (SVMs)
Maximum-margin classifiers, kernel trick, and SVM algorithms

Module #9
Clustering Analysis
K-means, hierarchical clustering, and density-based clustering methods

Module #10
Dimensionality Reduction
Principal Component Analysis (PCA), t-SNE, and feature selection

Module #11
Time Series Analysis
ARIMA, exponential smoothing, and prophet models for time series forecasting

Module #12
Anomaly Detection
Identifying outliers, density-based methods, and isolation forest

Module #13
Recommendation Systems
Collaborative filtering, content-based filtering, and hybrid approaches

Module #14
Natural Language Processing (NLP)
Text preprocessing, sentiment analysis, and topic modeling

Module #15
Deep Learning for Predictive Analytics
Introduction to deep learning, neural networks, and Keras/TensorFlow

Module #16
Model Evaluation and Selection
Metrics for evaluating predictive models, cross-validation, and hyperparameter tuning

Module #17
Model Deployment and Maintenance
Deploying predictive models, model monitoring, and continuous improvement

Module #18
Big Data Analytics for Predictive Analytics
Hadoop, Spark, and NoSQL databases for large-scale predictive analytics

Module #19
Predictive Analytics with Python
Using popular Python libraries for predictive analytics (scikit-learn, pandas, etc.)

Module #20
Predictive Analytics with R
Using popular R libraries for predictive analytics (caret, dplyr, etc.)

Module #21
Case Studies in Predictive Analytics
Real-world applications of predictive analytics in various industries

Module #22
Ethics and Fairness in Predictive Analytics
Bias detection, fairness metrics, and ethical considerations in predictive modeling

Module #23
Predictive Analytics in Finance
Applications of predictive analytics in finance, including risk management and portfolio optimization

Module #24
Predictive Analytics in Healthcare
Applications of predictive analytics in healthcare, including disease diagnosis and patient outcomes

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

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Predictive Analytics Techniques ( 25 Modules )

Predictive Analytics Techniques
( 25 Modules )