Chapters (15)
- 0:00Introduction
- 3:06Setup
- 4:29Linear regression (theory)
- 9:29Linear regression (Python)
- 20:59Classification (theory)
- 30:16Classifiaction (Python)
- 49:30Resampling & regularization (theory)
- 56:09Resampling and regularization (Python)
- 1:05:17Decision trees (theory)
- 1:13:12Decision trees (Python)
- 1:24:50SVM (theory)
- 1:28:17SVM (Python)
- 1:58:24Unsupervised learning (theory)
- 2:06:54Unsupervised learning (Python)
- 2:20:55Conclusion
Show the creator's full description
Learn the basics of Data Science in the crash course. You will learn about the theory and code behind the most common algorithms used in data science.
✏️ Course created by Marco Peixeiro. Check out his channel: https://www.youtube.com/channel/UC-0lpiwlftqwC7znCcF83qg
💻 Code: https://github.com/marcopeix/datasciencewithmarco
💻 Datasets: https://github.com/marcopeix/datasciencewithmarco/tree/master/data
⭐️ Course Contents ⭐️
⌨️ (00:00) Introduction
⌨️ (03:06) Setup
⌨️ (04:29) Linear regression (theory)
⌨️ (09:29) Linear regression (Python)
⌨️ (20:59) Classification (theory)
⌨️ (30:16) Classifiaction (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
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