Explore the powerful synergy between MATLAB and Excel in the realm of control systems, data analysis, and machine learning. By combining the computational capabilities of MATLAB with the familiar interface of Excel, you can effectively harness the power of PID control, leverage interoperability with Python, and utilise the Classification Learner App for intuitive classification tasks. Join us as we delve into the seamless integration of these tools to enhance your engineering and data analysis workflows.
Learn how to use MATLAB with Excel
PID controller tuning appears easy, but finding the set of proportional, integral, and derivative gains that ensures the best performance of your control system is a complex task.
MATLAB® and Simulink® make PID tuning easy, by letting you:
The MATLAB Engine API for Python bridges the capabilities of MATLAB and Python, enabling Python scripts to utilise MATLAB's extensive library of computational functions and visualisation tools directly. This approach is particularly powerful for users who wish to leverage MATLAB's superior numerical computing and graphical plotting capabilities within a Python workflow. By allowing the execution of MATLAB commands and scripts from within the Python environment, this API requires a licensed installation of MATLAB on the user’s machine. Users can call upon MATLAB's advanced algorithms, perform complex data analysis, and generate high-quality figures and plots, all without leaving the comfort of their Python development space.
The alternative to direct integration through the MATLAB Engine API for Python is the use of MATLAB's Library Compiler app to compile Python packages. This approach allows you to transform MATLAB algorithms and functions into standalone Python packages, which can be integrated into Python applications. The compiled Python package does not require a MATLAB license to run, but it does necessitate the installation of the MATLAB Runtime - a royalty-free set of libraries that enables the execution of compiled MATLAB applications or components on computers that do not have MATLAB installed.
Calling Python from MATLAB opens a vast array of possibilities, allowing users to leverage Python's extensive libraries and capabilities directly within the MATLAB environment. MATLAB provides a straightforward and efficient way to integrate Python code, enabling users to execute Python scripts, access Python functions, and even manipulate Python data types. All of this can be done by simply adding the relevant python scripts to the Python path in the MATLAB environment. This workflow is especially useful for users who are already working in MATLAB and want to use Python to solve part of their problem or for users who need functionality that is only available in Python.
The Classification Learner app in the Statistics and Machine Learning toolbox facilitates training classification models through supervised learning without requiring code. It supports tasks like importing data, exploring it, training multiple models, and evaluating their performance, including hyper parameter optimisation, adjusting misclassification costs, and selecting features. Users can import data from matrices or tables, with the app identifying predictors and response variables.
It offers a variety of classification algorithms, such as Decision Trees and Support Vector Machines, and provides tools like scatter plots for data exploration. The app simplifies model training and evaluation, displaying accuracy, confusion matrices, and ROC curves. It also allows for manual or automated feature selection and hyperparameter tuning to enhance model performance. Additionally, users can export their best models to the MATLAB workspace for predictions on new data or generate MATLAB code for model training or integration into applications. The app also supports generating C code for deploying models on unembedded devices or as software components in various applications. Comprehensive documentation and guidance for choosing the right classifier are readily accessible within the app.
The Classification Learner app provides an environment for training, testing, and optimising machine learning models, something far beyond Excel's capabilities. Excel is primarily a spreadsheet tool with limited support for advanced statistical and machine learning algorithms. MATLAB, on the other hand, is designed specifically for mathematical and scientific computing, offering a wide range of built-in functions and tools for much more complex analyses.
