Artificial Neural Network and Machine Learning using MATLAB

Learn to Create Neural Network with Matlab Toolbox and Easy to Follow Codes; with Comprehensive Theoretical Concepts

This course is uniquely designed to be suitable for both experienced developers seeking to make that jump to Machine learning or complete beginners who don’t understand machine learning and Artificial Neural Network from the ground up.

What you’ll learn

  • Develop a multilayer perceptron neural networks or MLP in MATLAB using Toolbox.
  • Apply Artificial Neural Networks in practice.
  • Building Artificial Neural Network Model.
  • Knowledge on Fundamentals of Machine Learning and Artificial Neural Network.
  • Understand Optimization methods.
  • Understand the Mathematical Model of a Neural Network.
  • Understand Function approximation methodology.
  • Make powerful analysis.
  • Knowledge on Performance Functions.
  • Knowledge on Training Methods for Machine Learning.

Course Content

  • Introduction –> 1 lecture • 3min.
  • Artificial Intelligence and Machine Learning –> 7 lectures • 25min.
  • Fundamentals of Artificial Neural Network –> 18 lectures • 1hr 12min.
  • MATLAB: Neural Net Fitting Tool –> 12 lectures • 59min.
  • MATLAB: Scripts –> 3 lectures • 28min.
  • MATLAB: Modified Advance Script –> 6 lectures • 38min.
  • MATLAB: Engine Data Set (Multiple Targets) –> 3 lectures • 28min.

Artificial Neural Network and Machine Learning using MATLAB

Requirements

  • Basics of Mathematics.

This course is uniquely designed to be suitable for both experienced developers seeking to make that jump to Machine learning or complete beginners who don’t understand machine learning and Artificial Neural Network from the ground up.

In this course, we introduce a comprehensive training of multilayer perceptron neural networks or MLP in MATLAB, in which, in addition to reviewing the theories related to MLP neural networks, the practical implementation of this type of network in MATLAB environment is also fully covered.

MATLAB offers specialized toolboxes and functions for working with Machine Learning and Artificial Neural Networks which makes it a lot easier and faster for you to develop a NN.

At the end of this course, you’ll be able to create a Neural Network for applications such as classification, clustering, pattern recognition, function approximation, control, prediction, and optimization.