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MachineLearningwithMATLABDuration: 2DaysLevel:MediumGAMAX Laboratory Solutions helps you overcome your complexengineering challenges. We help a broad spectrum of industriesto accelerate the innovation process in the field of R&D.As the sole authorized regional representative for Eastern Europesince 1996, we provide over two decades of expertise withMathworks, Comsol, and Speedgoat products, software, andtraining.We offer consultation in project planning and design, research,virtual prototyping, testing, and go to market simulations.
Machine Learning with MATLABThis two-day course focuses on data analytics and machinelearning techniques in MATLAB using functionality withinStatistics and Machine Learning Toolbox and Deep LearningToolbox. The course demonstrates the use of unsupervisedlearning to discover features in large data sets and supervisedlearning to build classification, predictive and regressive modelsand neural networks.Prerequisites:MATLAB FundamentalsTopicsDay 1Day 2 Importing and Organizing Data Finding Natural Patterns in Data Building Classification Models Improving Predictive Models Building Regression Models Creating Neural Networks
Training contentDay 1 of 2Importing and Organizing DataObjective: Bring data into MATLAB and organize it for analysis, including normalizingdata and removing observations with missing values. Data typesTablesCategorical dataData preparationFinding Natural Patterns in DataObjective: Use unsupervised learning techniques to group observations based on aset of explanatory variables and discover natural patterns in a data set. Unsupervised learningClustering methodsCluster evaluation and interpretationBuilding Classification ModelsObjective: Use supervised learning techniques to perform predictive modeling forclassification problems. Evaluate the accuracy of a predictive model. Supervised learningTraining and validationClassification methods
Day 2 of 2Improving Predictive ModelsObjective: Reduce the dimensionality of a data set. Improve and simplify machinelearning models. Cross validationHyperparameter optimizationFeature transformationFeature selectionEnsemble learningBuilding Regression ModelsObjective: Use supervised learning techniques to perform predictive modeling forcontinuous response variables. Parametric regression methodsNonparametric regression methodsEvaluation of regression modelsCreating Neural NetworkObjective: Create and train neural networks for clustering and predictive modeling.Adjust network architecture to improve performance. Clustering with Self-Organizing MapsClassification with feed-forward networksRegression with feed-forward networksShould you have more specific training needs, please contact usabout in-person and customized training opportunities:training@gamaxlabsol.com
Machine Learning with MATLAB This two-day course focuses on data analytics and machine learning techniques in MATLAB using functionality within Statistics and Machine Learning Toolbox and Deep Learning Toolbox. The course demonstrates the use of unsupervised learning to discover features in large data sets and supervised
