Support Vector Machines in Python - SVM in Python 2019
4.2 (223 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
51,477 students enrolled

Support Vector Machines in Python - SVM in Python 2019

Learn Support Vector Machines in Python. Covers basic SVM models to Kernel-based advanced SVM models of Machine Learning
4.2 (223 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
51,477 students enrolled
Last updated 5/2020
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Current price: $119.99 Original price: $199.99 Discount: 40% off
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This course includes
  • 6 hours on-demand video
  • 2 articles
  • 4 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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
Course content
Expand all 54 lectures 06:07:14
+ Setting up Python and Python Crash Course
10 lectures 01:38:02
Course resources
00:04
Opening Jupyter Notebook
09:06
Introduction to Jupyter
13:26
Arithmetic operators in Python: Python Basics
04:28
Strings in Python: Python Basics
19:07
Lists, Tuples and Directories: Python Basics
18:41
Working with Numpy Library of Python
11:54
Working with Pandas Library of Python
09:15
Working with Seaborn Library of Python
08:57
+ Machine Learning Basics
2 lectures 24:45
Introduction to Machine Learning
16:03
Building a Machine Learning Model
08:42
+ Maximum Margin Classifier
4 lectures 12:15
Course flow
01:34
The Concept of a Hyperplane
04:55
Maximum Margin Classifier
03:18
+ Support Vector Classifier
2 lectures 11:34
Limitations of Support Vector Classifiers
01:34
Quiz
1 question
+ Support Vector Machines
1 lecture 06:45
Kernel Based Support Vector Machines
06:45
Quiz
2 questions
+ Creating Support Vector Machine Model in Python
16 lectures 01:28:35
Regression and Classification Models
00:46
The Data set for the Regression problem
02:59
Importing data for regression model
05:40
Missing value treatment
03:38
Dummy Variable creation
04:58
X-y Split
04:02
Test-Train Split
06:04
Standardizing the data
06:28
SVM based Regression Model in Python
10:08
The Data set for the Classification problem
01:38
Classification model - Preprocessing
08:24
Classification model - Standardizing the data
01:57
SVM Based classification model
11:28
Hyper Parameter Tuning
09:47
Polynomial Kernel with Hyperparameter Tuning
04:07
Radial Kernel with Hyperparameter Tuning
06:31
+ Appendix 1: Data Preprocessing
18 lectures 02:04:52
Gathering Business Knowledge
03:26
Data Exploration
03:19
The Dataset and the Data Dictionary
07:31
Importing Data in Python
06:04
Univariate analysis and EDD
03:34
EDD in Python
12:11
Outlier Treatment
04:15
Outlier Treatment in Python
14:18
Missing Value Imputation
03:36
Missing Value Imputation in Python
04:57
Seasonality in Data
03:35
Bi-variate analysis and Variable transformation
16:14
Variable transformation and deletion in Python
09:21
Non-usable variables
04:44
Dummy variable creation: Handling qualitative data
04:50
Dummy variable creation in Python
05:45
Correlation Analysis
10:05
Correlation Analysis in Python
07:07
Requirements
  • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
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:

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

Who this course is for:
  • People pursuing a career in data science
  • Working Professionals beginning their Data journey
  • Statisticians needing more practical experience
  • Anyone curious to master SVM technique from Beginner to Advanced in short span of time