Aws Mlu Explain
Visual, Interactive Articles About Machine Learning: https://mlu-explain.github.io/
Overview:
The MLU-Explain repository contains educational articles on machine learning concepts presented visually and interactively. It serves as supplementary material for Machine Learning University (MLU), offering access to courses used to train Amazon's developers on machine learning.
Features:
- Linear Regression: A visual, interactive explanation of linear regression.
- Logistic Regression: Learn about how logistic regression can be used for binary classification.
- ROC & AUC: Visual explanation of the ROC curve, AUC, and their significance.
- Train, Test, and Validation Sets: Demonstrates the importance of data splitting in machine learning.
- Precision & Recall: Discusses evaluation metrics like Precision, Recall, F1-score, and Confusion Matrices.
- Random Forest: Covers the Random Forest algorithm and its application.
- Decision Trees: Explains the Decision Tree algorithm, splits, Entropy, and Information Gain.
- Bias Variance Tradeoff: Understands the tradeoff between under- and over-fitting models.
- Svelte
Svelte is a modern front-end framework that compiles your code at build time, resulting in smaller and faster applications. It uses a reactive approach to update the DOM, allowing for high performance and a smoother user experience.
