Basic Machine Learning for Robotics Course - Python

Machine Learning, Robotics

Course Summary

The course covers: ✅ LiDAR Navigation ✅ Feature Engineering & Clustering ✅ Data Augmentation ✅ Regression & Neural Networks ✅ Object Detection

What you will learn

Learn machine learning for robotics with TurtleBot4's LiDAR & RGB camera:

Course Overview

Unit1

Unit 1 is the entry point to your journey in Basic Machine Learning for Robotics.

Unit2

Machine Learning Overview/Basics: The main goal of this unit is to introduce key machine learning concepts, providing a solid foundation for more advanced topics in future units.

Unit3

Supervised Learning - (Introduction to Regression, Data Collection, and Initial Exploration): In this unit, we’ll embark on a journey to equip TurtleBot4 with foundational skills in regression and data handling for robotics.

UNIT 4

Supervised Learning - (Data Preprocessing, Feature Engineering, and Model Training): The goal of this unit is enabling TurtleBot4 to navigate even more effectively by refining and training models using its sensor data.

Unit5

Exploring Data Augmentation and Feature Engineering: This unit is all about exploring advanced techniques in data augmentation and feature engineering to enhance TurtleBot4's navigation capabilities.

Unit6

Object Detection & Classification & Tracking: This unit will equip the TurtleBot4 with the ability to detect objects in its environment using an RGB camera.

Teachers

Mark Bilginer

AI & Robotics Engineer, specializing in ROS2, TurtleBot4, LiDAR navigation & AI-driven perception to build intelligent robotic systems for real-world applications.