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Mo Rebaie
In this 1-hour long guided project-based course, you will learn how to use Python to implement a Support Vector Machine algorithm for classification. This type of algorithm classifies output data and makes predictions. The output of this model is a set of visualized scattered plots separated with a straight line.
You will learn the fundamental theory and practical illustrations behind Support Vector Machines and learn to fit, examine, and utilize supervised Classification models using SVM to classify data, using Python.
Read more
In this 1-hour long guided project-based course, you will learn how to use Python to implement a Support Vector Machine algorithm for classification. This type of algorithm classifies output data and makes predictions. The output of this model is a set of visualized scattered plots separated with a straight line.
You will learn the fundamental theory and practical illustrations behind Support Vector Machines and learn to fit, examine, and utilize supervised Classification models using SVM to classify data, using Python.
Read more
In this 1-hour long guided project-based course, you will learn how to use Python to implement a Support Vector Machine algorithm for classification. This type of algorithm classifies output data and makes predictions. The output of this model is a set of visualized scattered plots separated with a straight line.
You will learn the fundamental theory and practical illustrations behind Support Vector Machines and learn to fit, examine, and utilize supervised Classification models using SVM to classify data, using Python.
We will walk you step-by-step into Machine Learning supervised problems. With every task in this project, you will expand your knowledge, develop new skills, and broaden your experience in Machine Learning.
Particularly, you will build a Support Vector Machine algorithm, and by the end of this project, you will be able to build your own SVM classification model with amazing visualization.
In order to be successful in this project, you should just know the basics of Python and classification algorithms.
Register for this course and see more details by visiting:
OpenCourser.com/course/bkjsfi/support
What's inside
Syllabus
Support Vector Machine Classification in Python
In this guided project, you will learn how to create a Support Vector Machine Classification algorithm and use it to solve a supervised learning problem. By the end of this 2-hour long project, you will have built, trained, predicted, and visualized an SVM model that will be able to accurately classifies the output data and make useful predictions.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Applies supervised classification models through the Support Vector Machine algorithm to solve business problems
Provides hands-on experience in building a Support Vector Machine classification model using Python
Students are expected to have basic knowledge of Python and classification algorithms
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Reviews summary
Useful python svm classification course
Learners say Support Vector Machine Classification in Python is a good course for beginners to get a basic understanding of SVM principles, Python implementations, and predictions. The majority of reviews suggest that this is a useful course for quickly getting started. Reviewers also note that the instructor provides practical examples for applying SVM in real-world projects.
Practical examples
"This project is very much educative from start to finish and it enables a beginner to master some key concepts."
Recommended for beginners
"It's very good course for beginnes"
"Good for beginners."
"I like the way we got involved into practice by setting goals which are a bit challenging yet we want to achieve successfully."
Unclear instruction
"it was a little bit hard to get message, when he did not write the code for visualizing and copied it."
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 Machine Classification in Python with these
activities:
Organize Course Resources
Show steps
Establish a structured system to organize notes, assignments, quizzes, and exams, enhancing your ability to locate and review course materials effectively.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Categorize and label materials according to topic or type.
-
Maintain a consistent naming convention for files.
Review Linear Algebra and Optimization
Show steps
Strengthen your foundational knowledge in linear algebra and optimization, which are essential for understanding the mathematical underpinnings of SVM.
Browse courses on
Linear Algebra
Show steps
-
Revisit concepts of vector spaces, matrices, and linear transformations.
-
Review optimization techniques, including gradient descent and convex optimization.
Host a Study Group
Show steps
Foster collaborative learning and reinforce your understanding of SVM by engaging in discussions and problem-solving with peers.
Browse courses on
Machine Learning Algorithms
Show steps
-
Organize a group of classmates with diverse skill levels.
-
Establish regular meeting times and a study schedule.
-
Prepare discussion topics and exercises to facilitate learning.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Implement Support Vector Machine from scratch
Show steps
Reinforce the core concepts of SVM by implementing it from scratch, solidifying your understanding of the algorithm's inner workings.
Browse courses on
Support Vector Machine
Show steps
-
Review the mathematical foundations of SVM.
-
Design the algorithm's architecture and data structures.
-
Implement the optimization algorithm.
-
Test and validate your SVM implementation.
Explore SVM Applications in Industry
Show steps
Broaden your knowledge of SVM's practical applications by exploring real-world case studies and industry use cases.
Show steps
-
Identify industries where SVM is commonly used.
-
Research successful SVM implementations in these industries.
-
Analyze case studies and identify best practices.
Develop a Visual Representation of SVM
Show steps
Enhance your understanding and communication of SVM by creating a visual representation that explains its key concepts and functionality.
Browse courses on
Data Visualization
Show steps
-
Choose an appropriate visualization technique.
-
Design the visual representation, ensuring clarity and effectiveness.
-
Implement the visualization using a suitable software or tool.
Build a Personalized SVM Model
Show steps
Apply your SVM knowledge to a real-world problem by building a customized model that addresses a specific classification task.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a suitable dataset for your classification task.
-
Preprocess and explore the data to gain insights.
-
Train and evaluate your SVM model using appropriate metrics.
-
Optimize the model's parameters for improved performance.
-
Deploy and monitor your model in a practical setting.
Organize Course Resources
Show steps
Establish a structured system to organize notes, assignments, quizzes, and exams, enhancing your ability to locate and review course materials effectively.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Categorize and label materials according to topic or type.
- Maintain a consistent naming convention for files.
Review Linear Algebra and Optimization
Show steps
Strengthen your foundational knowledge in linear algebra and optimization, which are essential for understanding the mathematical underpinnings of SVM.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revisit concepts of vector spaces, matrices, and linear transformations.
- Review optimization techniques, including gradient descent and convex optimization.
Host a Study Group
Show steps
Foster collaborative learning and reinforce your understanding of SVM by engaging in discussions and problem-solving with peers.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Organize a group of classmates with diverse skill levels.
- Establish regular meeting times and a study schedule.
- Prepare discussion topics and exercises to facilitate learning.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Implement Support Vector Machine from scratch
Show steps
Reinforce the core concepts of SVM by implementing it from scratch, solidifying your understanding of the algorithm's inner workings.
Browse courses on
Support Vector Machine
Show steps
Show steps
Show steps
- Review the mathematical foundations of SVM.
- Design the algorithm's architecture and data structures.
- Implement the optimization algorithm.
- Test and validate your SVM implementation.
Explore SVM Applications in Industry
Show steps
Broaden your knowledge of SVM's practical applications by exploring real-world case studies and industry use cases.
Show steps
Show steps
Show steps
- Identify industries where SVM is commonly used.
- Research successful SVM implementations in these industries.
- Analyze case studies and identify best practices.
Develop a Visual Representation of SVM
Show steps
Enhance your understanding and communication of SVM by creating a visual representation that explains its key concepts and functionality.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose an appropriate visualization technique.
- Design the visual representation, ensuring clarity and effectiveness.
- Implement the visualization using a suitable software or tool.
Build a Personalized SVM Model
Show steps
Apply your SVM knowledge to a real-world problem by building a customized model that addresses a specific classification task.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a suitable dataset for your classification task.
- Preprocess and explore the data to gain insights.
- Train and evaluate your SVM model using appropriate metrics.
- Optimize the model's parameters for improved performance.
- Deploy and monitor your model in a practical setting.
Career center
Learners who complete Support Vector Machine Classification in Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
**Data Scientists** use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. They collaborate with domain experts and stakeholders to understand business problems and apply their expertise to develop data-driven solutions that leverage machine learning and other advanced analytical techniques. This course can provide a strong foundation for aspiring Data Scientists, as it introduces fundamental concepts of machine learning and provides practical experience in implementing a Support Vector Machine algorithm for classification problems.
Machine Learning Engineer
Machine Learning Engineer
**Machine Learning Engineers** design, develop, and deploy machine learning models to solve real-world problems. They work on the entire machine learning pipeline, from data collection and preparation to model training, evaluation, and deployment. This course can provide a valuable introduction to machine learning for aspiring Machine Learning Engineers, as it covers the theory and practical implementation of a Support Vector Machine algorithm, a widely used classification technique.
Data Analyst
Data Analyst
**Data Analysts** collect, clean, and analyze data to identify trends, patterns, and insights that can inform decision-making. They use various statistical and machine learning techniques to extract meaningful information from data. This course can be beneficial for aspiring Data Analysts, as it provides an introduction to machine learning and practical experience in implementing a Support Vector Machine algorithm for classification tasks.
Quantitative Analyst
Quantitative Analyst
**Quantitative Analysts** use mathematical and statistical models to analyze financial data and make investment decisions. They develop and implement trading strategies, risk management models, and other quantitative techniques to maximize returns and minimize risks. This course can provide a valuable foundation for aspiring Quantitative Analysts, as it introduces fundamental concepts of machine learning and provides practical experience in implementing a Support Vector Machine algorithm for classification problems.
Software Engineer
Software Engineer
**Software Engineers** design, develop, and maintain software systems. They work on various aspects of software development, including requirements gathering, design, coding, testing, and deployment. This course can be beneficial for Software Engineers who want to expand their knowledge in machine learning and gain practical experience in implementing a Support Vector Machine algorithm for classification tasks.
Statistician
Statistician
**Statisticians** collect, analyze, interpret, and present data to provide insights and make informed decisions. They use statistical methods and techniques to solve real-world problems in various fields, such as healthcare, finance, and marketing. This course can provide a valuable introduction to machine learning for Statisticians, as it covers the theory and practical implementation of a Support Vector Machine algorithm, a widely used classification technique.
Market Researcher
Market Researcher
**Market Researchers** conduct research to understand consumer behavior and market trends. They use various methods, including surveys, interviews, and data analysis, to collect and analyze data. This course can provide a valuable introduction to machine learning for Market Researchers, as it covers the theory and practical implementation of a Support Vector Machine algorithm, a widely used classification technique.
Business Analyst
Business Analyst
**Business Analysts** analyze business processes and systems to identify areas for improvement. They use data analysis techniques to identify trends, patterns, and insights that can help businesses make better decisions. This course can be beneficial for Business Analysts who want to expand their knowledge in machine learning and gain practical experience in implementing a Support Vector Machine algorithm for classification tasks.
Actuary
Actuary
**Actuaries** use mathematical and statistical models to assess and manage risk. They work in various fields, including insurance, finance, and healthcare, to evaluate the probability of future events and develop strategies to mitigate risks. This course can provide a valuable introduction to machine learning for Actuaries, as it covers the theory and practical implementation of a Support Vector Machine algorithm, a widely used classification technique.
Financial Analyst
Financial Analyst
**Financial Analysts** analyze financial data and make recommendations on investment decisions. They use various analytical techniques, including financial modeling and data analysis, to evaluate the financial performance of companies and make investment recommendations. This course can provide a valuable introduction to machine learning for Financial Analysts, as it covers the theory and practical implementation of a Support Vector Machine algorithm, a widely used classification technique.
Business Intelligence Analyst
Business Intelligence Analyst
**Business Intelligence Analysts** use data analysis techniques to identify trends, patterns, and insights that can help businesses make better decisions. They work with data from various sources, including internal data systems, market research, and social media, to provide insights that can improve business performance.
Data Engineer
Data Engineer
**Data Engineers** design, build, and maintain data pipelines and infrastructure. They work with data from various sources, including databases, data warehouses, and cloud storage, to ensure that data is available, reliable, and accessible for analysis and reporting. This course can provide a valuable introduction to machine learning for Data Engineers, as it covers the theory and practical implementation of a Support Vector Machine algorithm, a widely used classification technique.
Sales Manager
Sales Manager
**Sales Managers** are responsible for leading and managing sales teams to achieve sales targets. They work with sales representatives to develop sales strategies, monitor performance, and provide coaching and support. This course may be useful for Sales Managers who want to gain a better understanding of machine learning and its applications in sales.
Product Manager
Product Manager
**Product Managers** are responsible for the development and management of products. They work with various stakeholders, including engineers, designers, and marketing teams, to define product requirements, roadmap, and launch strategy. This course may be useful for Product Managers who want to gain a better understanding of machine learning and its applications in product development.
Marketing Manager
Marketing Manager
**Marketing Managers** are responsible for planning and executing marketing campaigns to promote products and services. They work with various teams, including sales, product development, and creative, to develop and implement marketing strategies. This course may be useful for Marketing Managers who want to gain a better understanding of machine learning and its applications in marketing.
For more career information including salaries, visit:
OpenCourser.com/course/bkjsfi/support
Reading list
We've selected 19 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 Machine Classification in Python.
Comprehensive introduction to support vector machines. It covers the theory and mathematics behind support vector machines, as well as their applications in classification and regression. The book is written in a clear and concise style, with plenty of examples and exercises.
Provides a comprehensive treatment of support vector machines, including their theory, algorithms, and applications. It valuable resource for researchers and practitioners.
Save
Provides a comprehensive treatment of pattern recognition and machine learning, including support vector machines. It valuable resource for researchers and practitioners.
Comprehensive guide to machine learning with Python. It covers all the essential concepts, from data preprocessing to model evaluation. The book is written in a clear and concise style, with plenty of examples and exercises.
Save
Provides a comprehensive treatment of pattern recognition, including support vector machines. It valuable resource for researchers and practitioners.
Save
Provides a comprehensive treatment of data mining, including support vector machines. It valuable resource for researchers and practitioners.
Classic introduction to statistical learning. It covers a wide range of topics, including data preprocessing, model training, and evaluation. The book is written in a clear and concise style, with plenty of examples and exercises.
Save
Comprehensive introduction to deep learning. It covers the theory and mathematics behind deep learning, as well as its applications in a variety of fields. The book is written in a clear and concise style, with plenty of examples and exercises.
Provides a comprehensive treatment of support vector machines, including their theory, algorithms, and applications. It valuable resource for a deeper understanding of the topic.
Provides a gentle introduction to machine learning with Python. It covers the basics of machine learning, including data preprocessing, model training, and evaluation. The book is written in a clear and concise style, with plenty of examples and exercises.
Comprehensive introduction to data mining with R. It covers a wide range of topics, including data preprocessing, model training, and evaluation. The book is written in a clear and concise style, with plenty of examples and exercises.
Provides a comprehensive overview of data mining techniques, including support vector machines. It valuable resource for a deeper understanding of the topic.
Provides a comprehensive overview of machine learning concepts and techniques, with a focus on Python implementation. It covers the fundamentals of supervised and unsupervised learning, including support vector machines.
Provides a practical introduction to machine learning using R, including support vector machines. It good starting point for those new to the field.
Provides a collection of practical recipes for implementing machine learning algorithms in Python, including support vector machines. It useful resource for hands-on experience.
Provides a comprehensive overview of machine learning concepts and techniques. It good starting point for those new to the field.
Provides a practical introduction to machine learning, including support vector machines. It good starting point for those new to the field.
Provides a practical introduction to machine learning, including support vector machines. It good starting point for those new to the field.
Great introduction to machine learning for beginners. It covers the basics of machine learning, including data preprocessing, model training, and evaluation. The book is written in a clear and concise style, with plenty of examples and exercises.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bkjsfi/support
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Mo Rebaie
Language
English
Good to know
Applies supervised classification models through the Support Vector Machine algorithm to solve business problems
Provides hands-on experience in building a Support Vector Machine classification model using Python
Students are expected to have basic knowledge of Python and classification algorithms
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