Build Machine Learning Models with MATLAB and Simulink Training by Tonex
Build machine learning models with MATLAB and Simulink is a comprehensive course that equips professionals with the knowledge and skills to leverage MATLAB and Simulink for building advanced machine learning models. Through examples and exercises and practical examples, participants will gain proficiency in using these powerful tools to develop and deploy machine learning solutions for a wide range of applications. Whether you are new to machine learning or seeking to enhance your existing skills, this course will empower you to harness the full potential of MATLAB and Simulink for machine learning.
Join us for this immersive training experience and unlock the potential of MATLAB and Simulink for building powerful machine learning models.
Learning Objectives: Upon completing this course, participants will be able to:
- Learn the fundamentals of machine learning and its applications.
- Master MATLAB and Simulink for data preprocessing, model development, and deployment.
- Build and train machine learning models using MATLAB’s comprehensive libraries.
- Implement various machine learning algorithms and techniques.
- Optimize and evaluate machine learning models for real-world scenarios.
- Deploy machine learning models in Simulink for integration into complex systems.
Audience: This course is designed for:
- Engineers and scientists seeking to apply machine learning to their projects.
- Data analysts and researchers looking to enhance their data analysis capabilities.
- Software developers interested in integrating machine learning into their applications.
- Professionals in the fields of robotics, automation, and control systems.
- Anyone aspiring to gain expertise in machine learning with MATLAB and Simulink.
Course Outline:
Introduction to Machine Learning with MATLAB
- Basics of machine learning concepts
- Introduction to MATLAB for machine learning
- Setting up the development environment
- Exploring MATLAB’s machine learning toolbox
- Hands-on: Data import and preprocessing with MATLAB
Supervised Learning with MATLAB
- Overview of supervised learning
- Linear and logistic regression
- Decision trees and ensemble methods
- Support vector machines (SVM)
- Neural networks with MATLAB
- Building and training supervised learning models
Unsupervised Learning and Clustering
- Introduction to unsupervised learning
- K-means clustering
- Hierarchical clustering
- Principal component analysis (PCA)
- Anomaly detection techniques
- Clustering and anomaly detection with MATLAB
Model Evaluation and Optimization
- Model evaluation metrics
- Cross-validation techniques
- Hyperparameter tuning
- Avoiding overfitting and underfitting
- Feature selection and engineering
- Model evaluation and optimization in MATLAB
Deploying Machine Learning Models with Simulink
- Introduction to Simulink for machine learning
- Integrating MATLAB models into Simulink
- Real-time and embedded system deployment
- Model verification and validation
- Case studies in Simulink deployment
- Deploying machine learning models in Simulink
Advanced Topics and Future Trends
- Transfer learning and deep learning
- Reinforcement learning with MATLAB
- Edge and cloud computing for ML
- Ethical considerations in machine learning
- Emerging trends and applications
- Exploring advanced machine learning concepts