Support Vector Machine Introduction

Welcome to the Support Vector Machine article, where you will learn both the concept behind an SVM model as well as its implementation of it in Python.

My name is Jagdish Chavan as a working Data Scientist and Industry Trainer, my Courses have been enrolled by over 1000 + students holding B.Sc in Computer Science with Mathematics and Statistics and I have experience in Building Projects in Data Science, ML, DL application and the consulting industry.

While doing my Job, I realized That many Data analysts and beginners in the field of data analytics are overwhelmed by SVMs. This article and coming articles focus will be on students who want to expand their skill set with this unconventional machine learning technique.

A practising data scientist needs to know from concept to code without getting too much mathematical about it. if you regularly put one hour a day within a week, you will be able to make support vector machine-based models and answer SVM-related interview questions.

The prerequisite for this series will be core and advanced python concepts then we will understand the concept and evolution of the maximum marginal classifier into a support classifier which further evolves into a support vector machine lately making two models one regression model and one classification model based on SVM. However, if you know some classic machine learning techniques such as liner regression or logistics regression you would be able to better appreciate the uniqueness of the SVM technique.

this article will be ideal for an existing Business analyst or data analyst who wants to expand on current skills or a student who wants to have a career in data science.

if you have any doubts or queries through the series you can post them in the comments, as soon as get free time I will be personally solving all your doubts, notebooks, code and data sets required for practice will be available on the dedicated GitHub repository.

The next article is dedicated to Setting up Python

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