Skills you'll gain

There are 9 modules in this course

Our journey is guided by the well-known textbook "The Elements of Statistical Learning" by T. Hastie, R. Tibshirani, and J. Friedman. This course provides examples written in Python. Your system should have Python 3.8 or higher, as well as essential libraries such as NumPy, pandas, matplotlib, seaborn, scikit-learn, SciPy, and PyTorch. These tools not only support the learning process but also prepare you for real-world data analysis challenges. Whether you're aiming to refine your expertise or just starting out in the field of data science, this course provides the knowledge and tools to transform your understanding and application of statistical learning. It's a perfect blend of theory and practice, ideal for anyone looking to enhance their skills in data interpretation and analysis.

Module 2: Linear Regression Methods

Module 3: Linear Classification Methods

Module 4: Basis Expansion Methods

Module 5: Kernel Smoothing Methods

Module 6: Model Assessment and Selection

Module 7: Maximum Likelihood Inference

Module 8: Advanced Topics

Summative Course Assessment