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Course: Machine Learning and Training Neural Network in MATLAB

Machine Learning and Training Neural Network in MATLAB

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
  • Self-Paced
About this Course

Machine Learning is the up and upcoming branch of Artificial Intelligence and it holds great promises for the generations to come. In this course, we will talk about Machine Learning and Artificial Neural Networks and how you can implement a simple Machine Learning Model in MATLAB.

Who this course is for:

  • Anyone who is interested in learning basic concepts of Machine Learning and Neural networks
Basic knowledge
  • The course is beginner level for those who are interested in implementing Machine Learning in MATLAB. No prior technical Knowledge is required. However, if you are already familiar with MATLAB, it can be a plus point
What you will learn
  • You will learn about Machine Learning and how you can train a simple Model in MATLAB on a simple Dataset. You will get to know some basics of MATLAB too and how you can write and run scripts in MATLAB. You will be able to import your own dataset and train it using different parameters to make some interactive prediction model
Curriculum
Number of Lectures: 6
Total Duration: 01:30:02
Introduction
  • Introduction to Machine Learning  

    A brief understanding of Machine Learning and it's applications.

  • Introduction to Artificial Neural Network  

    Description of Artificial Neural Networks and their resemblance to their Biological Counterparts

Data Pre Processing
  • Iris Data Download  

    Downloading Data from UCI Machine Learning Repository which is a freely available source for millions of different datasets.

  • Importing Data in MATLAB  
  • Pre Processsing in MATLAB  

    Here, data is converted into suitable format for the machine learning model. Using scripts in MATLAB, the target labels are converted into vector form and reorganized along with input data.

Training And Analyzing
  • Training the Model and Analyzing Results  

    Here, we will train the model by setting several hyperparameters and we will analyze the results.

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