Machine Learning First Steps Course

Learn fundamental machine learning concepts and applications in robotics through this comprehensive course covering essential algorithms, neural networks, and practical implementations. Master Python programming for machine learning applications and explore supervised learning techniques including linear regression with gradient descent optimization. Dive into classification methods using logistic regression and binary classification approaches with hands-on examples. Understand neural network architecture and implementation through practical exercises in robotics contexts. Explore computer vision applications including stereo vision depth estimation for robotic systems. Discover convolutional neural networks (CNNs) and their functionality in processing visual data for robotics applications. Apply machine learning concepts to real-world scenarios including robotic assistive devices and stereo vision systems. Gain practical experience with essential utilities and tools needed for machine learning development in robotics environments.

Syllabus

Introduction to Machine Learning (ML) | Machine Learning for Robotics | Lesson 1
Utilities Needed for Following Machine Learning Tutorials | Lesson 2
Python for Machine Learning in 40 Minutes | Machine Learning for Robotics | Lesson 3
Linear Regression for Machine Learning | Gradient Descent Algorithm | Supervised Learning | Lesson 4
Linear Regression for Machine Learning: a Case Study | Stereo Vision Depth Estimation | Lesson 5
Binary Classification and Logistic Regression in Machine Learning | Lesson 6
Classification in Machine Learning Example Using Logistic Regression | Lesson 7
Neural Network Architecture in Machine Learning | Lesson 8
Neural Network in Machine Learning Example | ML in Robotics Course | Lesson 9
How Convolutional Neural Networks (CNNs) Work | ML in Robotics Course | Lesson 10
CNNs Case Study on a Robotic Assistive Device | ML in Robotics Course | Lesson 11