Applied Artificial Intelligence: From Theory to Industry
Journey Overview
Upon completion of this journey, students will be equipped with the knowledge and skills to use the latest and most advanced machine learning and deep learning models to accurately and quickly identify human activities and gestures in real-time, detect objects in images and videos, identify faces, extract facial and pose landmarks as long as facial expressions and apply face recognition . At the end of the journey, students will be ready to create a marketable, real-world project.
Prerequisites
– Prior knowledge of Python
– Basic machine learning concepts
– Basic understanding of computer science concepts
Target Audience
– Individuals interested in artificial intelligence and its applications
– Python programmers looking to enhance their skills in AI
– Students pursuing a career in machine learning and AI
Content
– Setting up your environment to get started with Python
– Learning the fundamentals of Python programming
– Using the right tools for the job
– Identifying projects you can work on
– Detecting handmarks using Mediapipe
– Detecting Pose Landmarks using Mediapipe
– Classifying Trajectories and Gesture Recognition using Mediapipe and DollarPy
– Detecting Objects using Masks and OpenCV
– Detecting Objects using Markers of TUIO
– Detecting Objects using YOLO
– Using YOLO To detect custom objects
– Detecting faces in images using OpenCV
– Identifying facial landmarks using Dlib
– Detecting facial expressions, age, gender, and more
– Gaze Tracking
– Facial recognition for identification (e.g., Face ID)
Deployment
Project