Data Science


Support Vector Machines in Python: SVM Concepts & Code

Learn Support Vector Machines in Python. Covers basic SVM models to Kernel-based advanced SVM models of Machine Learning

4.32 (333 reviews)

Support Vector Machines in Python: SVM Concepts & Code



6 hours


Nov 2020

Last Update
Regular Price

What you will learn

Get a solid understanding of Support Vector Machines (SVM)

Understand the business scenarios where Support Vector Machines (SVM) is applicable

Tune a machine learning model's hyperparameters and evaluate its performance.

Use Support Vector Machines (SVM) to make predictions

Implementation of SVM models in Python


You're looking for a complete Support Vector Machines course that teaches you everything you need to create a Support Vector Machines model in Python, right?

You've found the right Support Vector Machines techniques course!

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through Decision tree.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

Go ahead and click the enroll button, and I'll see you in lesson 1!


Start-Tech Academy


Setting up Python and Python Crash Course

Installing Python and Anaconda

Course resources

Opening Jupyter Notebook

Introduction to Jupyter

Arithmetic operators in Python: Python Basics

Strings in Python: Python Basics

Lists, Tuples and Directories: Python Basics

Working with Numpy Library of Python

Working with Pandas Library of Python

Working with Seaborn Library of Python

Machine Learning Basics

Introduction to Machine Learning

Building a Machine Learning Model

Maximum Margin Classifier

Course flow

The Concept of a Hyperplane

Maximum Margin Classifier

Limitations of Maximum Margin Classifier

Support Vector Classifier

Support Vector classifiers

Limitations of Support Vector Classifiers


Support Vector Machines

Kernel Based Support Vector Machines


Creating Support Vector Machine Model in Python

Regression and Classification Models

The Data set for the Regression problem

Importing data for regression model

Missing value treatment

Dummy Variable creation

X-y Split

Test-Train Split

Standardizing the data

SVM based Regression Model in Python

The Data set for the Classification problem

Classification model - Preprocessing

Classification model - Standardizing the data

SVM Based classification model

Hyper Parameter Tuning

Polynomial Kernel with Hyperparameter Tuning

Radial Kernel with Hyperparameter Tuning

Bonus Section

Bonus Lecture

Appendix 1: Data Preprocessing

Gathering Business Knowledge

Data Exploration

The Dataset and the Data Dictionary

Importing Data in Python

Univariate analysis and EDD

EDD in Python

Outlier Treatment

Outlier Treatment in Python

Missing Value Imputation

Missing Value Imputation in Python

Seasonality in Data

Bi-variate analysis and Variable transformation

Variable transformation and deletion in Python

Non-usable variables

Dummy variable creation: Handling qualitative data

Dummy variable creation in Python

Correlation Analysis

Correlation Analysis in Python


Gaurang21 June 2020

This course does not explain theory behind machine learning concepts , rather it applies ML models using functions of pandas and numpy. It is a decent course who wishes to get familiar with data preprocessing on built-in datasets.

Pragyan8 June 2020

Till Now I find the audio and quality of video better. It should contain some theory of SVM at the beginning which made you loose 1 point.

Arvind20 April 2020

I joined for python course in Udemy , flexible timing. I got 100% of practical knowledge in python.Highly

Menna15 April 2020

This course was amazing, I enjoy it and learned a lot. The only comment for the instructors is the English accent wasn't very good, It takes awhile to understand what they say. Thank you

Mohan1 April 2020

Awesome courses for Vector Machine Courses. Provided with all lectures notes ,well explanation makes the best environments for learning .

Juari23 December 2019

Good course on anaconda data pre-processing and calculation but the lessons don't have a logical order (there are pratical tutorials in the beginning and introductions at the end)


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