Discover the Heart of Data Science: Regression Models
From sales forecasting to customer lifetime value prediction, from price optimization to risk scoring—regression models lie at the core of almost every data science project.
In this intensive 2-hour workshop, you will understand the mathematical foundations of regression models, compare different algorithms (Linear, Ridge, Lasso, Polynomial), and build predictive models working on real data.
From data preparation to feature engineering, from loss functions to metric optimization, and from overfitting to regularization techniques, you will experience core machine learning concepts from theory to practice and learn in depth how regression models work.
Instructors:
Who Should Attend
Beginners in data science – Those who want to build a solid foundation in machine learning and train their first predictive models.
University students – Students in statistics, mathematics, computer engineering, or related fields looking to turn theoretical knowledge into practice.
Professionals aiming for career growth – Data analysts, business analysts, or software developers looking to add ML skills to their portfolio.
Data professionals sharpening their fundamentals – Practitioners who want to deeply understand the mathematical foundations of regression models and compare algorithms systematically.
Enthusiasts with a technical background – Anyone with basic Python skills seeking a practical entry into data science.
Curriculum
Introduction to Data Science
The Concept of Machine Learning
Supervised vs. Unsupervised Learning
Training, Validation, and Test Datasets
Fundamentals of Regression Models
What Is Regression? How It Differs from Classification
Types of Regression Models
Model Evaluation Metrics (MSE, RMSE, MAE, R²)
Overfitting and Underfitting Concepts
Machine Learning Applications
Building a Machine Learning Pipeline
Feature Engineering Techniques
Model Comparison Strategies
Hyperparameter Tuning
Interpreting Results
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