| lesson-1.1.1-what-is-machine-learning-keynote.pdf | 379.2 KB | |
| lesson-1.1.10-step-5-modelling-part-3-tuning-a-model-keynote.pdf | 1 MB | |
| lesson-1.1.11-step-5-modelling-part-4-comparing-models-keynote.pdf | 1.1 MB | |
| lesson-1.1.12-step-6-experimentation-keynote.pdf | 469.8 KB | |
| lesson-1.1.13-tools-you-will-be-using-keynote.pdf | 1.3 MB | |
| lesson-1.1.2-what-we-will-focus-on-and-what-you-will-finish-with-keynote.pdf | 456.4 KB | |
| lesson-1.1.3-a-6-step-machine-learning-project-framework-keynote.pdf | 1 MB | |
| lesson-1.1.4-machine-learning-problem-definition-keynote.pdf | 13.1 MB | |
| lesson-1.1.5-step-2-data-keynote.pdf | 1.3 MB | |
| lesson-1.1.6-step-3-evaluation-keynote.pdf | 1.3 MB | |
| lesson-1.1.7-step-4-features-keynote.pdf | 30.3 MB | |
| lesson-1.1.8-step-5-modelling-part-1-3-sets-keynote.pdf | 740.8 KB | |
| lesson-1.1.9-step-5-modelling-part-2-choosing-a-model-keynote.pdf | 1 MB | |
| lesson-1.2.1-getting-your-computer-ready-for-machine-learning-with-anaconda-miniconda-and-conda-keynote.pdf | 1.6 MB | |
| lesson-1.2.2-what-is-conda-keynote.pdf | 988 KB | |
| lesson-1.2.3-creating-environments-with-miniconda-and-conda-keynote.pdf | 1.3 MB | |
| lesson-2.1.1-what-is-pandas.pdf | 1.4 MB | |
| lesson-2.2.2-what-is-numpy-keynote.pdf | 1.7 MB | |
| lesson-2.3.1-what-is-matplotlib-keynote.pdf | 2.7 MB | |
| lesson-2.4.1-what-is-scikit-learn-keynote.pdf | 3.7 MB | |
| lesson-3.0-introduction-to-classification-structured-data-projects.pdf | 3.6 MB | |
| lesson-3.2-introduction-to-regression-structured-projects.pdf | 3.3 MB | |
| lesson-4.0-what-is-tensorflow-and-introduction-to-unstructured-data-projects.pdf | 6.6 MB | |
| lesson-5.0-how-think-about-sharing-and-communicating-your-work.pdf | 6.5 MB | |
