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2-Regression/1-Tools/README.md

@@ -60,7 +60,7 @@ According to their [website](https://scikit-learn.org/stable/getting_started.htm
 
 > 🎓 **[Supervised Learning](https://en.wikipedia.org/wiki/Supervised_learning)** works by mapping an input to an output based on example pairs. It uses **labeled** training data to build a function to make predictions. [Download a printable Zine about Supervised Learning](https://zines.jenlooper.com/zines/supervisedlearning.html). Regression, which is covered in this group of lessons, is a type of supervised learning.
 
-> 🎓 **[Unsupervised Learning](https://en.wikipedia.org/wiki/Unsupervised_learning)** works similarly but it maps pairs using **unlabeled data**. [Download a printable Zine about Supervised Learning](https://zines.jenlooper.com/zines/unsupervisedlearning.html)
+> 🎓 **[Unsupervised Learning](https://en.wikipedia.org/wiki/Unsupervised_learning)** works similarly but it maps pairs using **unlabeled data**. [Download a printable Zine about Unsupervised Learning](https://zines.jenlooper.com/zines/unsupervisedlearning.html)
 
 > 🎓 **[Model Fitting](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py)** in the context of machine learning refers to the accuracy of the model's underlying function as it attempts to analyze data with which it is not familiar. **Underfitting** and **overfitting** are common problems that degrade the quality of the model as the model fits either not well enough or too well. This causes the model to make predictions either too closely aligned or too loosely aligned with its training data. An overfit model predicts training data too well because it has learned the data's details and noise too well. An underfit model is not accurate as it can neither accurately analyze its training data nor data it has not yet 'seen'.