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
Note: 4.6/5 (398 notes) 76,988 students
Instructor(s): Start-Tech Academy
Last update: 2022-01-15
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
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
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
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!
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
- Setting up Python and Python Crash Course
- Installing Python and Anaconda
- Course Resources
- Opening Jupyter Notebook
- This is a milestone!
- 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
- More about 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
- 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
- Bonus Section
- The final milestone!
- Bonus Lecture
Time remaining or 325 enrolls left
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