5 Days of Matlab, Simulink & SimScape – New 2022!
Build & Simulate Powerful Models in Matlab, Simulink & SimScape. Master Transfer Functions, PID, AI/ML & Live Scripts!
Created by Dr. Ryan Ahmed, Ph.D., MBA | 6 hours on-demand video course
Do you want to learn Matlab, Simulink, and Simscape in a practical, easy, fun, and engaging way? Do you want to build powerful simulation models in Simulink & Simscape but don’t know where to start? Are you an absolute beginner who want to break into modeling/simulation in Simulink and Simscape and looking for a course that has everything you need? If the answer is yes to any of these questions, then this course is for you!
Simulink is a powerful MATLAB-based tool used to design and simulate mechanical, electrical, electromechanical, and hydraulic systems using a large comprehensive drag and drop library. This course is unique and exceptional in many ways. Every day, we will spend 1-2 hours together and learn how to build and simulate Matlab, Simulink and simscape models. The course is designed in a unique project-based learning approach, and you will learn by doing! The course includes several practice opportunities, quizzes, and final capstone projects.
What you’ll learn
- Model and simulate Mechanical Systems Using Physical Modeling techniques in SimScape
- Model and Simulate Systems in the Time Domain Using Simulink
- Analyze Systems Response and Change Model Parameters Using Matlab Live Scripts
- Understand the Basic Concepts of Control Systems and Develop their Own Controllers in Simulink
- Understand Control System Theory, Develop PID controllers, and Tune Controllers in Simulink
- Convert Systems from Time Domain to S-Domain Using Laplace Transform
- Understand the Concept of Transfer Functions
- Understand the Impact of Stiffness and Damping Coefficients on Second-Order Systems
- Understand the Theory and Intuition Behind Artificial Intelligence, Machine Learning, Deep Learning, and Artificial Neural Networks
- Train Artificial Intelligence Classifier Models in Matlab to Detect and Classify Diabetes and Cancer diseases
- Understand classifier model Key Performance Indicators (KPIs) such as accuracy, Area Under Curve (AUC) and Receiver Operating Characteristic (ROC) Curve
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