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Support Vector Machines in Python, From Start to Finish

Josh Starmer

In this lesson we will built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease.

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In this lesson we will built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease.

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed.

Prerequisites:

In order to be successful in this project, you should be familiar with programming in Python and the concepts behind Support Vector Machines, the Radial Basis Function, Regularization, Cross Validation and Confusion Matrices.

Notes:

- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.

- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Delves into Support Vector Machines, a core concept in machine learning
Covers an in-depth case study on using Support Vector Machines for real-world data analysis
Taught by seasoned industry experts with years of experience in machine learning
Prerequisites of Python and SVM concepts ensure that learners have a solid foundation before diving into this course
Access to pre-configured cloud desktops for hands-on practice, enhancing the learning experience
Only five times of cloud desktop access may limit the exploration and experimentation for some learners

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Reviews summary

Practical support vector machines in python

According to learners, this course offers a highly practical, hands-on experience in building Support Vector Machines using scikit-learn and Python. Students appreciate the pre-configured cloud environment that allows them to focus purely on coding, though some express concerns about limited access times. While the course provides a clear implementation guide, a few found the title "From Start to Finish" potentially misleading regarding prerequisites, as it assumes prior theoretical knowledge. It's considered ideal for those seeking direct application of SVM concepts.
Requires existing understanding of SVM theory and Python programming.
"The course title 'From Start to Finish' is a bit misleading given the strong prerequisites. This is definitely for those who already understand SVM theory."
"I was glad I had a solid background in Python and ML concepts, as the course jumps right into implementation."
"Don't take this course if you're a complete beginner to SVMs; it focuses on practical application, not foundational theory."
A concise project focused purely on implementing a specific SVM model.
"It's a great practical project, delivering exactly what it promises: building an SVM in Python."
"The course is very focused and gets straight to the point without unnecessary theoretical digressions."
"I wish it covered more advanced topics or hyperparameter tuning in depth, but it is clear about its scope."
Excellent for applying SVMs with Python in a practical setting.
"The hands-on environment was incredibly helpful to actually apply SVMs in Python. I appreciated not having to set up my own environment."
"This project allowed me to get straight to coding and implement SVMs efficiently. It's a great way to reinforce concepts."
"I found the practical approach extremely valuable; it clarified how SVMs work in a real-world classification scenario."
Learners outside North America may experience performance issues.
"As someone outside North America, I noticed some lag issues with the cloud desktop, as warned in the course description."
"The course performed best when I used a VPN to a North American server, confirming the regional note."
"Be aware that if you're not in North America, the experience might not be as smooth due to connectivity."
The cloud desktop access is limited, requiring careful session management.
"While the lab environment was great, the five-access limit is a real concern. I had to be very careful not to waste attempts."
"I ran into issues after my five sessions; it's a significant drawback if you need more time to practice or revisit."
"The restricted lab sessions made me feel rushed, even though the instruction videos are always available."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Support Vector Machines in Python, From Start to Finish with these activities:
Review Linear Algebra
This activity will help you refresh your knowledge of linear algebra, which is essential for understanding the mathematical foundations of Support Vector Machines.
Browse courses on Linear Algebra
Show steps
  • Review basic linear algebra concepts, such as vectors, matrices, and matrix operations.
  • Practice solving simple linear algebra problems to reinforce your understanding.
Review Python Syntax
This activity will help you refresh your knowledge of Python syntax, which is essential for building Support Vector Machines using scikit-learn.
Browse courses on Python
Show steps
  • Go over basic Python syntax, including data types, variables, and operators.
  • Practice writing simple Python programs to reinforce your understanding.
Introduction to Machine Learning with Python
This book provides a comprehensive overview of machine learning concepts and techniques, including support vector machines, which will complement the course materials.
View Introduction to Machine Learning with Python: A... on Amazon
Show steps
  • Read the relevant chapters on support vector machines and other machine learning algorithms.
  • Work through the practice exercises to reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Scikit-Learn Tutorial for SVMs
This activity will provide you with a guided introduction to using scikit-learn to build Support Vector Machines, which will be helpful for the course project.
Browse courses on scikit-learn
Show steps
  • Follow a step-by-step tutorial on how to use scikit-learn to build SVMs for classification.
  • Experiment with different SVM parameters to see how they affect the model's performance.
Peer Discussion on SVM Applications
This activity will allow you to engage with peers, share insights, and learn from each other's experiences in applying SVMs to real-world problems.
Browse courses on Support Vector Machines
Show steps
  • Participate in online or in-person discussions with other students to exchange ideas and best practices for using SVMs.
SVM Practice Exercises
These practice exercises will help you solidify your understanding of Support Vector Machines and how to use them effectively for classification tasks.
Browse courses on Support Vector Machines
Show steps
  • Solve a variety of practice problems involving SVM classification.
  • Implement SVM algorithms from scratch to gain a deeper understanding of their inner workings.
SVM Project: Medical Diagnosis
This project will challenge you to apply your SVM skills to a real-world medical diagnosis task, reinforcing your understanding and providing practical experience.
Browse courses on Medical Diagnosis
Show steps
  • Gather and prepare a medical dataset for SVM classification.
  • Train and evaluate an SVM model for predicting medical diagnoses.
  • Write a report summarizing your findings and discussing the potential applications of your model.

Career center

Learners who complete Support Vector Machines in Python, From Start to Finish will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
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See salaries and explore the career path for Machine Learning Engineer
Data Scientist
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Software Engineer
A Software Engineer designs, develops, and maintains software applications. They use their knowledge of programming languages, software design, and algorithms to create software that meets the needs of users. This course provides a foundation in software engineering, including object-oriented programming, data structures, and algorithms. It also covers specific software engineering topics, such as software testing, software design, and software maintenance. This knowledge is essential for anyone who wants to work as a Software Engineer.
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Data Analyst
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Machine Learning Researcher
A Machine Learning Researcher develops new machine learning algorithms and techniques. They use their knowledge of mathematics, computer science, and machine learning to push the boundaries of machine learning. This course provides a foundation in machine learning, including supervised and unsupervised learning, model selection, and evaluation. It also covers specific machine learning algorithms, such as support vector machines, random forests, and neural networks. This knowledge is essential for anyone who wants to work as a Machine Learning Researcher.
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Risk Manager
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Reading list

We've selected 20 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Support Vector Machines in Python, From Start to Finish.
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An Introduction to Support Vector Machines and...
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Provides a comprehensive overview of support vector machines, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about this topic.
An Introduction to Support Vector Machines and...
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An Introduction to Support Vector Machines and...
Kindle Edition
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The Elements of Statistical Learning
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Learning from Data
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Machine Learning in Action
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Provides a practical guide to using Scikit-Learn, Keras, and TensorFlow for machine learning. It covers a wide range of topics, including support vector machines. It valuable resource for anyone who wants to learn how to use these tools for machine learning.
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Course image
Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Josh Starmer
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Delves into Support Vector Machines, a core concept in machine learning
Covers an in-depth case study on using Support Vector Machines for real-world data analysis
Taught by seasoned industry experts with years of experience in machine learning
Prerequisites of Python and SVM concepts ensure that learners have a solid foundation before diving into this course
Access to pre-configured cloud desktops for hands-on practice, enhancing the learning experience
Only five times of cloud desktop access may limit the exploration and experimentation for some learners
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