Machine learning (ML) isn’t science fiction; it’s a powerful tool that helps businesses leverage the power of data. Imagine computers capable of self learning and improvement , revealing concealed patterns and providing data-driven predictions. That’s the enchantment of ML.
Harness the power of data and unlock new possibilities for your business with TechSource Asia’s machine learning solutions. We empower businesses to leverage cutting-edge machine learning techniques to gain deeper insights, automate tasks, and achieve significant results.
Machine Learning Solutions with TechSource Asia
Machine learning teaches machines to do what comes naturally to humans: learn from experience. Use MATLAB to engineer features from your data and fit machine learning models.
Why MATLAB for Machine Learning?Classify Data Using the Classification Learner App Interactively explore your data, select features, and train, compare, and assess models by using the Classification Learner and Regression Learner apps. Integrate with Simulink Systems Integrate your trained models with Simulink as native or MATLAB Function blocks, for embedded deployment or simulation of complete systems. Deploy Trained Models to Hardware Deploy your trained models to hardware platforms (from desktop systems to embedded hardware) by generating readable and portable C/C++ code. |
 |
|||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||
|
||||||||||||||||||||||||||||||||||||||||||
See How Others Use MATLAB for Machine Learning
Read Ebook
Machine Learning with MATLAB (Techniques and algorithms)
DOWNLOAD
Interactive Apps and Algorithms
Choose from a wide variety of the most popular classification, clustering, and regression algorithms – now also “shallow” neural nets (up to three layers) alongside other machine learning models. Use classification and regression apps to interactively train, compare, tune, and export models for further analysis, integration, and deployment. If writing code is more your style, you can further optimize models with feature selection and parameter tuning.
Model Interpretability
Overcome the black-box nature of machine learning by applying established interpretability methods such as Partial Dependence plots, LIME, Shapley values, and Generalized Additive Model (GAM). Validate that the model is using the right evidence for its predictions and find model biases that were not apparent during training.
Automated Machine Learning (AutoML)
Automatically generate features from training data and optimize models using hyperparameter tuning techniques such as Bayesian optimization. Use specialized feature extraction techniques such as wavelet scattering for signal or image data, and feature selection techniques such as neighborhood component analysis (NCA), minimum redundancy maximum relevance (MRMR) or sequential feature selection.
Code Generation and Simulink Integration
Deploy statistics and machine learning models to embedded systems and generate readable C or C++ code for your entire machine learning algorithm, including pre and post processing steps. Accelerate verification and validation of your high-fidelity simulations using machine learning models through MATLAB function blocks and native blocks in Simulink.
Scaling & Performance
Use tall arrays train machine learning models to data sets too large to fit in memory, with minimal changes to your code. You can also speed up statistical computations and model training with parallel computing on your desktop, on clusters, or on the cloud.
Unlock the Potential of Machine Learning
Let TechSource Asia be your trusted partner in your machine learning journey. Contact us today to discuss your specific requirements and explore how machine learning can transform your business.
Contact Us
If you have any enquiry, please do not hesitate to contact us.
Contact Us
