Ahmed Imam

Naive Bayes Algorithm is one of the most famous supervised machine learning algorithms for the multi-classification problems.

Some of its applications

  • Text Classifications to determine to which class a text document belongs to (i.e. politics, comics, etc).
  • Spam Mailing Prediction System
  • Sentiment Analysis like for daily rating of tweets, reviews and comments on social media to determine if they are violent, sarcastic, and so on.
  • Medical Diagnosis

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Back_propagation

Tip: You should have good understanding of what is supervised machine learning, what is training data and testing data. (Also, I prefer to read the first part of this topic

  • As per supervised machine learning, we have inputs and actual output (labeled data)
  • We are using preliminary random weight values.

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SVM non-linear models and kernel-tricks

In the first part of this tutorial regarding SVM-algorithm linear model which I strongly recommend to read first, it was mentioned that SVM is used for solving both regression and classification problems and mostly used for classification as it has a great ability to classify by using either linear or non-linear modeling.

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Linear Model

Introduction

In machine learning, support vector machines (SVMs; also, support vector networks) are supervised learning models with associated learning algorithms that analyze data and recognize patterns used for classification and regression analysis but mostly with classification analysis. It works by establishing a hyperplane between different classes of data. If the data can’t be linearly separated, SVM will try to project data into a higher dimension in away it can be separated with what is known as kernels trick.

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