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

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

Description

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:


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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!

Cheers

Start-Tech Academy

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Content

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
Quiz
Support Vector Machines
Kernel Based Support Vector Machines
Quiz
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