The Perl and Raku Conference (In the Cloud)

Everything You Always Wanted to Know About Machine Learning In Perl
(But Were Afraid To Ask)

What is Machine Learning?
Why is ML important?
How does ML work?
How is ML different from Artificial Intelligence?
How can ML benefit your Perl projects?

Learn the answers to these questions, and more!

In this talk, we will learn the basics of Machine Learning in pure Perl, including concepts such as the following...

The 4 types of ML algorithms:
- supervised
- semi-supervised
- unsupervised
- reinforcement

Common ML algorithms:
- K-Nearest Neighbor
- Learning Vector Quantization
- Self-Organizing Map
- Locally Weighted Learning
- Linear Regression
- Logistic Regression
- Decision Tree
- Naive Bayes
- K-Means Clustering
- Support Vector Machine
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boosting algorithms

We will review Perl source code samples from select algorithms, run live demos, and analyze the results.

If you have always been curious about Machine Learning in Perl, or if you are already an ML programmer looking to expand your knowledge, then this talk is for you!