Course Overview
- Course Title: Certified Predictive Modeling & Regression
- Instructor: Muhammad Shafiq (Data Scientist | AI & ML Engineer | Lecturer | Researcher)
- Target Audience:
- Aspiring data scientists and analysts
- Professionals seeking predictive modeling certification
- Business analysts aiming to apply regression techniques to real-world problems
- Students preparing for data science or statistics exams
- Intermediate learners with basic statistics or programming knowledge
- Prerequisites:
- Basic understanding of statistics (mean, variance, distributions)
- Familiarity with data analysis concepts (recommended but not mandatory)
Curriculum Highlights
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Key Topics Covered:
- Simple & Multiple Linear Regression (OLS method, interpretation of coefficients)
- Assumption Testing (homoscedasticity, multicollinearity, normality of residuals)
- Model Evaluation Metrics (R-squared, adjusted R-squared, RMSE, MAE)
- Logistic Regression (odds ratios, probability thresholds, classification metrics)
- Model Diagnostics (residual analysis, leverage points, influence measures)
- Advanced Regression Techniques:
- Stepwise Regression (forward, backward, bidirectional selection)
- Regularization Methods (Lasso, Ridge regression for overfitting)
- Cross-Validation (k-fold, LOOCV for model robustness)
- Classification Performance Metrics (AUC-ROC, confusion matrix, precision-recall)
- Certification-Ready Topics (professional reporting, model validation, ethical considerations)
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Key Skills Learned:
- Building and interpreting linear and logistic regression models
- Diagnosing and addressing violation of regression assumptions
- Selecting optimal models using statistical and machine learning techniques
- Evaluating model performance with industry-standard metrics
- Applying regularization to prevent overfitting
- Preparing for predictive modeling certifications (theoretical and practical)
- Translating regression outputs into actionable business insights
Course Format
- Duration:
- 3 practice tests (self-assessment)
- Self-paced (lifetime access to materials)
- Format:
- On-demand video lectures (mobile and TV accessible)
- Conceptual framework (applicable to R, Python, Excel, SPSS, SAS)
- Resources:
- Downloadable slides (theory and case studies)
- Hands-on exercises (conceptual, tool-agnostic)
- Quizzes (reinforcement of key concepts)
- Certification of Completion (Udemy-issued)