Learn the basics of Data Science in the crash course created by Marco Peixeiro, where you will learn about the theory and code behind the most common algorithms used in data science.

Datasets:

Course Contents

- ⌨️ (00:00) Introduction
- ⌨️ (03:06) Setup
- ⌨️ (04:29) Linear regression (theory)
- ⌨️ (09:29) Linear regression (Python)
- ⌨️ (20:59) Classification (theory)
- ⌨️ (30:16) Classification (Python)
- ⌨️ (49:30) Resampling & regularization (theory)
- ⌨️ (56:09) Resampling and regularization (Python)
- ⌨️ (1:05:17) Decision trees (theory)
- ⌨️ (1:13:12) Decision trees (Python)
- ⌨️ (1:24:50) SVM (theory)
- ⌨️ (1:28:17) SVM (Python)
- ⌨️ (1:58:24) Unsupervised learning (theory)
- ⌨️ (2:06:54) Unsupervised learning (Python)
- ⌨️ (2:20:55) Conclusion