Learn the basics of Data Science through this hands-on crash course. The course covers the theory and practical implementation of common algorithms such as linear regression, classification, decision trees, SVM, and unsupervised learning. By the end of the course, you will have gained knowledge and practical experience in using Python for data science tasks. The course is designed for beginners looking to kickstart their journey in the field of Data Science.
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
