Learn the details of different types of Machine Learning algorithms and tune them to optimize their performance for any application. While AI Basics covers how to use different algorithms in Regression and Classification, here we describe how they work internally and how to tune them. We also cover introduce an important new type of algorithm. Neural Networks (Deep Learning), which is used to understand images, sound and video.
Topics, Tools, and Modules:
Learn the details of ML algorithms, specifically
KNN
Decision Trees
Linear Regression
Neural Networks
Build and tune the hyper-parameters of these ML algorithms to build end-to-end working systems. Learn what hyper-parameters are, now they control the behavior of these algorithms, and how to modify them. Learn core concepts of regularization and feature engineering.
Learn to understand the type of problem and suitable algorithm in publicly available datasets such as Kaggle.
Train an AI to learn and compete in a target shooting game. Teach your AI to point correctly at the target. You can use either active game play or mathematics to create your training set.
Bring your own data and build a custom AI driven application in a code lab. You can program in any language you like. We will provide examples in Python, Scratch, Java and Javascript.
Prerequisites
Bring your own Laptop to class
The AI Basics class (M1) or students in Grade 8. Please contact info@pyxeda.ai if you are not sure about satisfying this requirement.
Basic linear algebra and probability background will be useful, but not a strict requirement.
What Students Take Away
Certificate of Completion from AIClub.
A cloud account that they can use to build new ML and AI applications using their laptops, tablets or other devices.
An AIClub membership where they can access new projects, showcase their code, and participate in competitions. Completing the advanced course will equip them with the skills they need to develop and showcase more advanced projects, and compete in advanced competitions.