Data Analytics, Data Modelling and Machine Learning with MATLAB

Who Should Attend?

Engineers, researchers, scientist, and managers, who are involved in the data analytic, data modeling and designing of intelligent systems that can automatically produce models which can analyze bigger, more complex data and deliver faster and more accurate results

Duration: 4 Days

The course demonstrates the use of appropriate MATLAB and Statistics and Machine Learning Toolbox functionality throughout the analysis process from importing and organizing data, to exploratory analysis, to confirmatory analysis and simulation as well as the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results.
Topics include:

  • Managing data
  • Calculating summary statistics
  • Visualizing data
  • Fitting distributions
  • Performing tests of significance
  • Performing analysis of variance
  • Fitting regression models
  • Reducing data sets
  • Generating random numbers and performing simulation
  • Organizing and preprocessing data
  • Clustering data
  • Creating classification and regression models
  • Interpreting and evaluating models
  • Simplifying data sets
  • Using ensembles to improve model performance
  • Learn to perform exploratory and confirmatory analysis and Monte Carlo simulations on the Data
  • Learn to find natural patterns in Data
  • Learn to build classification models
  • Learn to improve and simplify machine learning models by reducing dimensionality of a data set
  • Learn to create and train neural networks for clustering and predictive modelling

Refer Quick Overview of MATLAB and Relevant Toolboxes
Part 1: Statistical Methods in MATLAB
Part 2: Machine Learning with MATLAB

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