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Snehan Kekre
In this project, you will learn the functioning and intuition behind a powerful class of supervised linear models known as support vector machines (SVMs). By the end of this project, you will be able to apply SVMs using scikit-learn and Python to your own classification tasks, including building a simple facial recognition model.
Read more
In this project, you will learn the functioning and intuition behind a powerful class of supervised linear models known as support vector machines (SVMs). By the end of this project, you will be able to apply SVMs using scikit-learn and Python to your own classification tasks, including building a simple facial recognition model.
Read more
In this project, you will learn the functioning and intuition behind a powerful class of supervised linear models known as support vector machines (SVMs). By the end of this project, you will be able to apply SVMs using scikit-learn and Python to your own classification tasks, including building a simple facial recognition model.
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 Python, Jupyter, and scikit-learn pre-installed.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/4e9plz/support
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What's inside
Syllabus
Project: Support Vector Machines with scikit-learn
Welcome to this project-based course on the Support Vector Machines with scikit-learn. In this project, you will learn the functioning and intuition behind a powerful class of supervised linear models known as support vector machines (SVMs). By the end of this project, you will be able to apply SVMs using scikit-learn and Python to your own classification tasks, including building a simple facial recognition model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches SVM classification, a powerful technique in machine learning
Provides hands-on experience with scikit-learn, an essential Python library for machine learning
Builds a practical skillset for data scientists and machine learning practitioners
Applies SVM algorithms to real-world tasks, including facial recognition
Requires prior knowledge of Python and machine learning concepts
Only offers access to the cloud desktop five times
Save this course
Save Support Vector Machines with scikit-learn to your list so you can find it easily later:
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Reviews summary
Svm with scikit-learn: concepts + guided project
Learners say this support vector machine course is a beginner-friendly project that includes engaging assignments. It's a great way to get a practical approach and learn more about support vector machines. Keep in mind that you may need a basic understanding of support vector machine to fully benefit from the guided project.
Supportive and understanding instructor.
"The instructor really guides you throughout the course of the project."
"I am grateful to have the chance to participate in an online course like this!"
"The instructor has mastery over these topics. I really enjoyed the session!"
Concepts are explained well.
"Nicely thaught concepts"
"Beginner friendly and walks you through most of major steps which are usually done in Machine Learning Projects with SVM"
"Good for the beginners"
Interface could be improved.
"content was good, but interface was not user friendly"
"some modules shown in the course are no longer in the present version"
Assumes some background knowledge.
"It might be difficult for some people to understand this course who have zero knowledge of machine learning."
"you need familiar background of all the libraries and a bit of knowledge from your part on support vector machines"
"I would advise you to gather a good knowledge on this topic to get the most out of this course."
Could use more detail in some areas.
"content was good, but interface was not user friendly"
"kernel trick that was mentioned is not talked about"
"Need more thorpugh explanation of python libraries and functions"
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 with scikit-learn with these
activities:
Review the concepts of machine learning
Show steps
Refreshes students' knowledge of machine learning, which is essential for understanding support vector machines.
Browse courses on
Machine Learning
Show steps
-
Review the notes from your previous machine learning course.
-
Work through a few machine learning tutorials.
-
Take a practice quiz on machine learning concepts.
Review Linear Algebra
Show steps
Builds a strong foundation in Linear Algebra, which is essential for understanding the concepts of support vector machines.
View
Linear Algebra Done Right (Undergraduate Texts...
on Amazon
Show steps
-
Read the first three chapters of the book.
-
Work through the exercises in the first three chapters.
-
Attend the first two lectures on Linear Algebra for Computer Science.
-
Complete the first homework assignment on Linear Algebra.
Organize your course materials
Show steps
Keeps students organized and helps them to stay on top of the course material.
Show steps
-
Create a folder for the course.
-
Download all of the course materials.
-
Organize the materials into subfolders.
-
Review the materials regularly.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve SVM practice problems
Show steps
Provides opportunities to apply the concepts of support vector machines to solve real-world problems.
Browse courses on
SVM
Show steps
-
Attempt to solve 5-10 SVM problems from a reputable source.
-
Review your solutions with the Instructor or a Teaching Assistant for feedback.
-
Identify and correct any errors in your understanding of SVM concepts.
Write a blog post on SVM applications
Show steps
Encourages students to think critically about the applications of SVMs and to communicate their understanding to others.
Browse courses on
SVM
Show steps
-
Research different applications of SVMs.
-
Choose a specific application to focus on.
-
Write a blog post that explains the application and how SVMs are used.
-
Publish your blog post and share it with others.
Build a simple facial recognition model
Show steps
Provides hands-on experience in applying SVMs to a practical task, reinforcing the concepts learned in the course.
Browse courses on
Facial Recognition
Show steps
-
Gather a dataset of images of faces.
-
Preprocess the images and extract features.
-
Train an SVM model to classify the images.
-
Evaluate the performance of the model.
Follow tutorials on advanced SVM techniques
Show steps
Keeps students up-to-date with the latest developments in SVM research and applications.
Browse courses on
SVM
Show steps
-
Identify a reputable source for SVM tutorials.
-
Select 2-3 tutorials that cover advanced SVM techniques.
-
Work through the tutorials, taking notes and experimenting with the code.
-
Discuss your findings with classmates or the Instructor.
Attend an SVM workshop
Show steps
Provides an opportunity to learn from experts in the field and to network with other SVM practitioners.
Browse courses on
SVM
Show steps
-
Identify and register for an upcoming SVM workshop.
-
Attend the workshop and actively participate in the discussions.
-
Connect with other attendees and exchange ideas.
-
Follow up with any contacts you made at the workshop.
Review the concepts of machine learning
Show steps
Refreshes students' knowledge of machine learning, which is essential for understanding support vector machines.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review the notes from your previous machine learning course.
- Work through a few machine learning tutorials.
- Take a practice quiz on machine learning concepts.
Review Linear Algebra
Show steps
Builds a strong foundation in Linear Algebra, which is essential for understanding the concepts of support vector machines.
View
Linear Algebra Done Right (Undergraduate Texts...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book.
- Work through the exercises in the first three chapters.
- Attend the first two lectures on Linear Algebra for Computer Science.
- Complete the first homework assignment on Linear Algebra.
Organize your course materials
Show steps
Keeps students organized and helps them to stay on top of the course material.
Show steps
Show steps
Show steps
- Create a folder for the course.
- Download all of the course materials.
- Organize the materials into subfolders.
- Review the materials regularly.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve SVM practice problems
Show steps
Provides opportunities to apply the concepts of support vector machines to solve real-world problems.
Browse courses on
SVM
Show steps
Show steps
Show steps
- Attempt to solve 5-10 SVM problems from a reputable source.
- Review your solutions with the Instructor or a Teaching Assistant for feedback.
- Identify and correct any errors in your understanding of SVM concepts.
Write a blog post on SVM applications
Show steps
Encourages students to think critically about the applications of SVMs and to communicate their understanding to others.
Browse courses on
SVM
Show steps
Show steps
Show steps
- Research different applications of SVMs.
- Choose a specific application to focus on.
- Write a blog post that explains the application and how SVMs are used.
- Publish your blog post and share it with others.
Build a simple facial recognition model
Show steps
Provides hands-on experience in applying SVMs to a practical task, reinforcing the concepts learned in the course.
Browse courses on
Facial Recognition
Show steps
Show steps
Show steps
- Gather a dataset of images of faces.
- Preprocess the images and extract features.
- Train an SVM model to classify the images.
- Evaluate the performance of the model.
Follow tutorials on advanced SVM techniques
Show steps
Keeps students up-to-date with the latest developments in SVM research and applications.
Browse courses on
SVM
Show steps
Show steps
Show steps
- Identify a reputable source for SVM tutorials.
- Select 2-3 tutorials that cover advanced SVM techniques.
- Work through the tutorials, taking notes and experimenting with the code.
- Discuss your findings with classmates or the Instructor.
Attend an SVM workshop
Show steps
Provides an opportunity to learn from experts in the field and to network with other SVM practitioners.
Browse courses on
SVM
Show steps
Show steps
Show steps
- Identify and register for an upcoming SVM workshop.
- Attend the workshop and actively participate in the discussions.
- Connect with other attendees and exchange ideas.
- Follow up with any contacts you made at the workshop.
Career center
Learners who complete Support Vector Machines with scikit-learn will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use machine learning, statistics, and data analysis to solve complex business problems. Support Vector Machines (SVMs) with scikit-learn is a powerful technique for classification tasks, which is a fundamental skill for Data Scientists. By taking this course, you'll learn the principles of SVMs, gain hands-on experience implementing them using Python and scikit-learn, equipping you with the skills necessary to excel in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models and algorithms. SVMs are a widely used algorithm for classification tasks, and scikit-learn is a leading library for implementing machine learning in Python. This course provides a solid understanding of SVMs and their implementation, enhancing your skills as a Machine Learning Engineer.
Data Analyst
Data Analyst
Data Analysts analyze data to extract insights and inform decision-making. SVMs are a valuable tool for identifying patterns and classifying data, which are essential skills for Data Analysts. The hands-on experience in this course will deepen your understanding of SVMs and enable you to apply them effectively in your role.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. SVMs are increasingly used in quantitative finance for tasks such as risk assessment and portfolio optimization. This course provides a foundation in SVMs, enabling you to explore their applications in quantitative analysis.
Research Scientist
Research Scientist
Research Scientists conduct research in various scientific fields. SVMs are a powerful tool for pattern recognition and classification, which are essential tasks in many areas of research. This course will provide you with a solid understanding of SVMs, empowering you to apply them in your research.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software applications. SVMs are used in various software applications for tasks such as image recognition and spam filtering. This course will equip you with the knowledge of SVMs and their implementation, enabling you to contribute effectively to software development projects involving machine learning.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. SVMs are a powerful tool for statistical classification and pattern recognition. This course will enhance your statistical skills by providing a solid understanding of SVMs and their applications in statistics.
Financial Analyst
Financial Analyst
Financial Analysts evaluate the financial performance of companies and make investment recommendations. SVMs are used in financial analysis for tasks such as credit scoring and fraud detection. This course will provide you with the knowledge of SVMs and their implementation, enabling you to gain an edge in the competitive field of financial analysis.
Actuary
Actuary
Actuaries assess and manage financial risks. SVMs are used in actuarial science for tasks such as insurance pricing and risk modeling. This course will provide you with the skills in SVMs and their applications, enhancing your abilities as an Actuary.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to biological and medical data. SVMs are used in biostatistics for tasks such as disease diagnosis and drug discovery. This course will expand your biostatistical toolkit by providing a solid understanding of SVMs and their applications in the field.
Market Researcher
Market Researcher
Market Researchers analyze market data to understand consumer behavior and trends. SVMs are used in market research for tasks such as customer segmentation and product recommendation. This course will provide you with the knowledge of SVMs and their implementation, enabling you to derive valuable insights from market data.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data infrastructure and systems. SVMs are used in data engineering for tasks such as data cleaning and transformation. This course will provide you with the skills in SVMs and their implementation, enabling you to contribute effectively in data engineering projects.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to improve efficiency and productivity. SVMs are used in business analysis for tasks such as customer churn prediction and fraud detection. This course will provide you with the knowledge of SVMs and their implementation, enabling you to gain a competitive edge in the field of business analysis.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve complex business problems. SVMs are used in operations research for tasks such as supply chain optimization and production planning. This course will provide you with the skills in SVMs and their implementation, enhancing your capabilities as an Operations Research Analyst.
Risk Manager
Risk Manager
Risk Managers assess and manage risks in various industries. SVMs are used in risk management for tasks such as risk identification and risk assessment. This course will provide you with the knowledge of SVMs and their implementation, enabling you to enhance your expertise in risk management.
For more career information including salaries, visit:
OpenCourser.com/course/4e9plz/support
Reading list
We've selected 13 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 with scikit-learn.
Provides comprehensive coverage of statistical learning and data mining techniques.
Save
Provides a comprehensive overview of machine learning algorithms and techniques.
Provides a comprehensive overview of machine learning and pattern recognition.
Save
Provides a comprehensive overview of neural networks and deep learning.
Save
Covers data mining techniques and methods.
Focuses on real-world applications of SVM.
Provides intuitive explanations of machine learning concepts.
Covers a broad range of artificial intelligence topics, including machine learning.
Provides a comprehensive overview of probabilistic graphical models.
Provides a comprehensive overview of reinforcement learning techniques.
Save
Covers deep learning techniques and architectures.
Covers natural language processing techniques and algorithms.
Save
Covers computer vision algorithms and techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4e9plz/support
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Effort
2.5 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Teaches SVM classification, a powerful technique in machine learning
Provides hands-on experience with scikit-learn, an essential Python library for machine learning
Builds a practical skillset for data scientists and machine learning practitioners
Applies SVM algorithms to real-world tasks, including facial recognition
Requires prior knowledge of Python and machine learning concepts
Only offers access to the cloud desktop five times
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