Support Vector Machines (SVM) in Python
Learn Support Vector Machines in Python. Covers basic SVM models to Kernel-based advanced SVM models of Machine Learning
Development,Data Science,ML,Python
Lectures -36
Resources -1
Duration -4 hours
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Course 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.
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.
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
Goals
What will you learn in this course:
- 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
Prerequisites
What are the prerequisites for this course?
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
Curriculum
Check out the detailed breakdown of what’s inside the course
Setting up Python
10 Lectures
- Installing Python and Anaconda 03:04 03:04
- Opening Jupyter Notebook 09:06 09:06
- Course Resources
- Introduction to Jupyter 13:26 13:26
- Arithmetic operators in Python: Python Basics 04:28 04:28
- Strings in Python: Python Basics 19:07 19:07
- Lists, Tuples and Directories: Python Basics 18:41 18:41
- Working with Numpy Library of Python 11:54 11:54
- Working with Pandas Library of Python 09:15 09:15
- Working with Seaborn Library of Python 08:57 08:57
Machine Learning Basics
2 Lectures
Maximum Margin Classifier
4 Lectures
Support Vector Classifier
2 Lectures
Support Vector Machines
1 Lectures
Creating Support Vector Machine Model in Python
16 Lectures
Instructor Details
Abhishek and Pukhraj
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