Further reading
Check out the following resources for more information on the topics that were covered in this chapter:
- A Beginner's Guide to Deep Reinforcement Learning: https://pathmind.com/wiki/deep-reinforcement-learning
- An Introduction to Gradient Descent and Linear Regression: https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/
- Assumptions of Multiple Linear Regression: https://www.statisticssolutions.com/assumptions-of-multiple-linear-regression/
- Clustering: https://scikit-learn.org/stable/modules/clustering.html
- Generalized Linear Models: https://scikit-learn.org/stable/modules/linear_model.html
- Guide to Interpretable Machine Learning – Techniques to dispel the black box myth of deep learning: https://towardsdatascience.com/guide-to-interpretable-machine-learning-d40e8a64b6cf
- In Depth: k-Means: https://jakevdp.github.io/PythonDataScienceHandbook/05.11-k-means.html
- Interpretable Machine Learning –...
