Dive into a comprehensive 2-3 hour crash course on data science fundamentals. Explore the theory and practical implementation of key algorithms used in the field. Begin with an introduction and setup, then progress through linear regression, classification, resampling and regularization, decision trees, support vector machines (SVM), and unsupervised learning. Each topic is covered in both theoretical and Python-based practical sessions. Access accompanying code and datasets on GitHub for hands-on practice. Gain a solid foundation in data science techniques and their real-world applications through this intensive, code-focused learning experience.
Overview
Syllabus
) Introduction.
) Setup.
) Linear regression (theory).
) Linear regression (Python).
) Classification (theory).
) Classifiaction (Python).
) Resampling & regularization (theory).
) Resampling and regularization (Python).
) Decision trees (theory).
) Decision trees (Python).
) SVM (theory).
) SVM (Python).
) Unsupervised learning (theory).
) Unsupervised learning (Python).
) Conclusion.
Taught by
freeCodeCamp.org
