Completed Google Course: Regression Analysis – Simplify Complex Data Relationships
I’m proud to share that I’ve successfully completed the Regression Analysis: Simplify Complex Data Relationships course, authorized by Google and offered through Coursera.
Regression is one of the core pillars of Machine Learning. Beyond simple prediction, it forms the foundation for modeling relationships, understanding feature impact, and building interpretable AI systems.
This course strengthened my ability to move from raw data to mathematically grounded predictive models, with a strong emphasis on interpretation and validation.
Throughout the program, I reinforced key ML-relevant competencies, including:
- Linear and multiple regression modeling: building models that capture complex feature relationships.
- Model evaluation & diagnostics: interpreting R², residual analysis, and statistical significance.
- Feature impact interpretation: understanding coefficients, effect size, and variable influence.
- Assumptions & robustness analysis: identifying multicollinearity, heteroscedasticity, and overfitting risks.
- From correlation to causation awareness: critically analyzing model conclusions and avoiding misleading interpretations.
Regression techniques remain central in my work across predictive systems, recommender components, and performance modeling. Strengthening my statistical and regression foundation enhances the rigor and reliability of the Machine Learning systems I design and deploy.