Learn Support Vector Machines (SVM) in Python and R
Want to learn the popular machine learning algorithm - Support Vector Machines (SVM)? Support Vector Machines can be used to build both Regression and Classification Machine Learning models.
This free course will not only teach you basics of Support Vector Machines (SVM) and how it works, it will also tell you how to implement it in Python and R.
This course on SVM would help you understand hyperplanes and Kernel tricks to leave you with one of the most popular machine learning algorithms at your disposal.
Here are key questions you should know the answer to:
Support Vector Machines (SVM) can be a tricky topic to learn if you aren’t asking the right questions. Here are a few key questions about SVM you should know:
- What is a Support Vector Machine (SVM)?
- Is SVM a must-know algorithm in machine learning?
- What kind of machine learning problems can I solve using SVM?
- Can I perform both classification and regression using SVM?
- What do I need to know before learning SVM?
- What is a SVM classifier?
- Can I rely on the Support Vector Machine algorithm in a Kaggle or DataHack hackathon?
- How difficult is it to learn SVM for a beginner in machine learning?
- What is a SVM kernel?
If you are not sure about any of the above questions, it’s the right time to enroll in this free course and start your SVM learning!
Course curriculum
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1
Introduction to Support Vector Machines
- What are Support Vector Machines?
- Why do we use SVM and how is it better?
- AI&ML Blackbelt Plus Program (Sponsored)
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2
How does SVM work?
- Non-linear Separation and Margins
- Hyperplanes in SVM
- Quiz: Support Vector Machine
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3
SVM Kernels and Hyperparameters
- Types of Kernels used in SVM
- Quiz: Kernel Tricks
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4
Implementing SVM in Python
- Hyperparameter tuning in SVM
- Implementing Support Vector Machine
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5
Implementing SVM in R
- How to implement Support Vector Machine Classifier in R?
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6
Challenges of SVM
- Drawbacks of SVM
- What next?
Instructor(s)
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Analytics Vidhya
Analytics Vidhya provides a community based knowledge portal for Analytics and Data Science professionals. The aim of the platform is to become a complete portal serving all knowledge and career needs of Data Science Professionals.
What does this Support Vector Machine (SVM) course cover?
Support Vector Machine (SVM) can appear as a complex topic if you’re going about it the wrong way. This course is designed in a structured manner to ensure you learn SVM in an easy to understand way. We have also included exercises and a popular machine learning project to help you gain a practical understanding of Support Vector Machines.
Here are the highlights of this Support Vector Machine (SVM) in Python and R course:
- Support Vector Machine (SVM) basics
- What is SVM?
- What is a SVM classifier?
- Why should you use SVM?(Advantages of SVM)
- How does the Support Vector Machine algorithm work?
- What are the different SVM hyperparameters?
- What is a SVM kernel?
- The different SVM kernels:
- SVM linear kernel
- SVM RBF kernel
- Implementing SVM in Python using the sklearn.svm.svc function
- Implementing SVM in R using the e1071 package
- Challenges you might face while implementing SVM in machine learning
This course on Support Vector Machines (SVM) is a taste of the various machine learning algorithms out there. We recommend taking the ‘Applied Machine Learning’ course to get the full package!
Who is this Support Vector Machine (SVM) in Python and R course for?
This course is designed for:
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Anyone who wants to start their machine learning career and is looking to learn the different machine learning algorithms
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Anyone who wants to become a data scientist or a machine learning engineer
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Anyone interested in learning how a classic machine learning algorithm like SVM works!
What do I need to start with Support Vector Machine in Python & R course?
- A working laptop / desktop with 8 GB RAM
- A working Internet connection
- Basic knowledge of Machine Learning
- Basic knowledge of Python / R - check out this Course first, if you are new to Python
This is all it takes for you to learn one of the most powerful algorithm in Machine Learning.
What are you waiting for?
Common Questions Beginners ask about Support Vector Machine (SVM)
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What is a Support Vector Machine (SVM)?
“Support Vector Machine” (SVM) is a supervised machine learning algorithm that can be used for both classification or regression problems. SVM is one of the most popular algorithms in machine learning and we’ve often seen interview questions related to this being asked regularly.
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Is SVM a must-know algorithm in machine learning?
SVM is definitely an algorithm every data scientist or machine learning engineer should know. It is a widely adopted algorithm across organizations.
Even on a personal learning front, learning SVM and how it works will add a lot to your own machine learning knowledge. -
Can I perform both classification and regression using SVM?
Yes! As we mentioned above, you can use Support Vector Machine (SVM) for both classification and regression problems. However, SVM is popularly used far more for classification tasks.
This is something you will learn inside the course as well. You will get an intuitive understanding as to why SVM gives such an accurate output when we apply it on classification problems. -
What kind of machine learning problems can I solve using SVM?
Support Vector Machine can be used for a wide variety of classification problem like, text classification, default loan prediction. You can try implementing SVM on different kinds of datasets to learn it in detail (https://datahack.analyticsvidhya.com/contest/all/)
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What do I need to know before learning SVM?
There are three key components you would need to know before jumping to SVM:
A working knowledge of Python (we recommend the Python for Data Science course)
If not Python, then a working knowledge of R
Basics of Machine Learning (what is supervised learning, what is classification and regression, etc.). You can take the ‘Introduction to Data Science’ course to learn all of these concepts -
What is an SVM classifier?
The SVM classifier is basically a machine learning algorithm which is used for classification tasks. We mainly use it for classifying data which can’t be separated by a straight line.
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Can I use Support Vector Machine algorithm in a Kaggle or DataHack hackathon?
Of course! You should always try out as many algorithms as you can in machine learning competitions. We always encourage our community to experiment on DataHack and Kaggle projects and hackathons.
You can apply what you learn in this course on Kaggle and DataHack hackathons for sure. -
How difficult is it to learn SVM for a beginner in machine learning?
SVM can be tricky….if you aren’t paying attention. If you follow the modules and lessons in this course, you’ll be a Support Vector Machine expert in a few hours!
Remember - practice is key. The more you practice your newly acquired SVM knowledge, the better you will become. Apply your learning on the various classification projects on the DataHack platform. -
What is a SVM kernel?
Since SVM is primarily used to classify non-linearly separable data, it provides a variety of functions to segregate the classes. These functions are called kernels. You can study more about these functions in the course and observe how SVM works.
Project - Iris Species Classification
Use Support Vector Machines algorithms to classify species of Iris flowers
You will analyse what features of an Iris flower can be used to classify its species for e.g. petal length, sepal length etc. You will build a classifier using Support Vector Machines (SVM)
Frequently Asked Questions
Common questions related to Support Vector Machines in Python & R course
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Who should take Support Vector Machines in Python & R course?
This course is for people who wants to learn one of the most popular machine learning algorithm - Support Vector Machines along with its implementation in Python and R.
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I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me?
The course assumes prior background in Machine Learning. So we would recommend you to be aware of basics of Machine Learning before going through this course.
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What is the fee for this course?
This course is free of cost.
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How long would I have access to "Support Vector Machines in Python and R" course?
Once you register, you will have 6 month access to complete the course. If you visit the course 6 month after your initial registration - you will need to enroll in the course again. Your past progress will be lost.
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How much effort will this course take?
You can complete Support Vector Machines in Python and R in a few hours. You are also expected to apply SVM to build a machine learning classifier.
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How can I apply and test my learnings about Support Vector Machines?
You can start by doing the tests at the end of various chapters. In addition, you can apply Support Vector Machines to solve various Practice problems on Analytics Vidhya DataHack Platform
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Can I download videos from this course?
We regularly update "Support Vector Machines in Python and R" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos.
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Which programming language is used to teach Support Vector Machines in this course?
This course teaches Support Vector Machines in both Python and R.
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Do I get a certificate upon completion of the course?
No, there is no certificate for this course.
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I just completed Support Vector Machine course, what should I do next?
Congratulations! We would highly recommend that you continue your machine learning journey by taking our Applied Machine Learning Course
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I don't have Python Installed in my machine, what can I do?
You can go ahead and install Anaconda distribution - it will come pre-installed with everything you need including Pandas and scikit-learn libraries
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How is a Free Course different from a paid course on Analytics Vidhya?
Our free courses are just the tip of the iceberg. They are good to get you started, where as paid course provide you with the depth required for industry roles.
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Can I add this project on my resume and use it in my interview?
Support Vector Machine is a very important algorithm in Machine Learning. Interviewers often ask questions about Kernel tricks and SVM implementation. You should go ahead and showcase your learning today.