5 Reasons to Learn Probability for Machine Learning
Last Updated on November 8, 2019
Probability is a field of mathematics that quantifies uncertainty.
It is undeniably a pillar of the field of machine learning, and many recommend it as a prerequisite subject to study prior to getting started. This is misleading advice, as probability makes more sense to a practitioner once they have the context of the applied machine learning process in which to interpret it.
In this post, you will discover why machine learning practitioners should study probabilities to improve their skills and capabilities.
After reading this post, you will know:
- Not everyone should learn probability; it depends where you are in your journey of learning machine learning.
- Many algorithms are designed using the tools and techniques from probability, such as Naive Bayes and Probabilistic Graphical Models.
- The maximum likelihood framework that underlies the training of many machine learning algorithms comes from the field of probability.
Kick-start your project with my new book Probability for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.
Let’s get started.