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Ashish Dikshit
Linear SVM Classification(Soft Margin) -using Scikit Learn
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What's inside
Syllabus
Project Overview
In this 1-hour long project-based course, you will learn 'Support Vector Machines' model in Machine Learning ,we will focus to perform linear SVM Classification. (Set up the environment, Illustrate Large Margin Classification and Sensitivity to features & Outliers, Demonstrate Large margin vs margin violations using Python Code and scientific libraries).This project gives you easy access to the invaluable learning techniques used by experts. This project favors a hands on approach, growing an intuitive understanding of machine learning through concrete working examples and just a little bit of theory .
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on a crucial machine learning approach, 'Support Vector Machines', providing beneficial understanding for data classification tasks in real-world scenarios
Utilizes a hands-on approach by providing interactive materials like Python code and scientific libraries, allowing learners to actively engage with the concepts
Suitable for learners with basic knowledge of Python and data science concepts
Save this course
Save Linear SVM Classification(Soft Margin) -using Scikit Learn to your list so you can find it easily later:
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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 Linear SVM Classification(Soft Margin) -using Scikit Learn with these
activities:
Review linear algebra and optimization
Show steps
Refreshing your knowledge of linear algebra and optimization will provide a strong foundation for understanding SVM concepts.
Browse courses on
Linear Algebra
Show steps
-
Review key concepts from linear algebra, such as vectors, matrices, and linear transformations.
-
Revisit optimization techniques, including gradient descent and convex optimization.
Organize and review course materials
Show steps
Organizing and reviewing course materials will help you retain information, identify gaps in your understanding, and prepare for assessments.
Show steps
-
Gather all course materials, including lecture notes, slides, assignments, and readings.
-
Review and summarize key concepts and ideas from each module.
Review 'An Introduction to Statistical Learning'
Show steps
This book provides a comprehensive overview of statistical learning, including SVM and related techniques, which can complement your understanding of the course material.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read specific chapters or sections related to SVM and machine learning concepts.
-
Take notes and highlight key insights to reinforce your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review 'Support Vector Machines'(SVM)
Show steps
Reviewing this book, will introduce you to the theory and algorithms of support vector machines (SVMs) and their applications in a variety of fields, including machine learning, pattern recognition, and bioinformatics.
View
Kernel Methods for Pattern Analysis
on Amazon
Show steps
-
Read chapters 1-3 of the book to gain an overview of SVMs.
-
Work through the exercises in chapters 1-3 to test your understanding of the material.
-
Apply the SVM algorithms to a real-world data set.
Engage in peer discussions on SVM
Show steps
Exchanging ideas and perspectives with peers can enhance your understanding and identify areas where you need further clarification.
Show steps
-
Join or create a study group or online forum dedicated to SVM.
-
Actively participate in discussions, asking questions, sharing insights, and providing feedback.
Complete practice problems on SVM
Show steps
Practicing SVM-related problems will strengthen your understanding of the concepts and techniques covered in the course.
Show steps
-
Identify a set of practice problems or exercises related to SVM.
-
Work through the problems step-by-step, implementing SVM algorithms using Python and Scikit-Learn.
-
Check your solutions against provided answer keys or online resources.
Create a visual representation of SVM
Show steps
Creating a visual representation will enhance your comprehension of SVM's concepts and their geometric interpretation.
Show steps
-
Choose a visualization method, such as a flowchart, diagram, or animation.
-
Identify key SVM concepts and relationships to be represented visually.
-
Create a visually appealing and informative representation using appropriate tools.
Develop a small-scale SVM project
Show steps
Applying SVM concepts to a practical project will solidify your understanding and demonstrate your proficiency in using SVM algorithms.
Show steps
-
Define a problem or dataset that can be addressed using SVM.
-
Collect or prepare the necessary data for training and testing.
-
Implement an SVM model using Python and Scikit-Learn.
-
Evaluate the performance of the SVM model and make necessary adjustments.
Review linear algebra and optimization
Show steps
Refreshing your knowledge of linear algebra and optimization will provide a strong foundation for understanding SVM concepts.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review key concepts from linear algebra, such as vectors, matrices, and linear transformations.
- Revisit optimization techniques, including gradient descent and convex optimization.
Organize and review course materials
Show steps
Organizing and reviewing course materials will help you retain information, identify gaps in your understanding, and prepare for assessments.
Show steps
Show steps
Show steps
- Gather all course materials, including lecture notes, slides, assignments, and readings.
- Review and summarize key concepts and ideas from each module.
Review 'An Introduction to Statistical Learning'
Show steps
This book provides a comprehensive overview of statistical learning, including SVM and related techniques, which can complement your understanding of the course material.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read specific chapters or sections related to SVM and machine learning concepts.
- Take notes and highlight key insights to reinforce your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review 'Support Vector Machines'(SVM)
Show steps
Reviewing this book, will introduce you to the theory and algorithms of support vector machines (SVMs) and their applications in a variety of fields, including machine learning, pattern recognition, and bioinformatics.
View
Kernel Methods for Pattern Analysis
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 of the book to gain an overview of SVMs.
- Work through the exercises in chapters 1-3 to test your understanding of the material.
- Apply the SVM algorithms to a real-world data set.
Engage in peer discussions on SVM
Show steps
Exchanging ideas and perspectives with peers can enhance your understanding and identify areas where you need further clarification.
Show steps
Show steps
Show steps
- Join or create a study group or online forum dedicated to SVM.
- Actively participate in discussions, asking questions, sharing insights, and providing feedback.
Complete practice problems on SVM
Show steps
Practicing SVM-related problems will strengthen your understanding of the concepts and techniques covered in the course.
Show steps
Show steps
Show steps
- Identify a set of practice problems or exercises related to SVM.
- Work through the problems step-by-step, implementing SVM algorithms using Python and Scikit-Learn.
- Check your solutions against provided answer keys or online resources.
Create a visual representation of SVM
Show steps
Creating a visual representation will enhance your comprehension of SVM's concepts and their geometric interpretation.
Show steps
Show steps
Show steps
- Choose a visualization method, such as a flowchart, diagram, or animation.
- Identify key SVM concepts and relationships to be represented visually.
- Create a visually appealing and informative representation using appropriate tools.
Develop a small-scale SVM project
Show steps
Applying SVM concepts to a practical project will solidify your understanding and demonstrate your proficiency in using SVM algorithms.
Show steps
Show steps
Show steps
- Define a problem or dataset that can be addressed using SVM.
- Collect or prepare the necessary data for training and testing.
- Implement an SVM model using Python and Scikit-Learn.
- Evaluate the performance of the SVM model and make necessary adjustments.
Career center
Learners who complete Linear SVM Classification(Soft Margin) -using Scikit Learn will develop knowledge and skills
that may be useful to these careers:
Data Science Manager
Data Science Manager
Data Science Managers lead teams of data scientists and oversee the development and implementation of machine learning models. They use linear SVM classification and other techniques to ensure that data science projects are successful. This course on linear SVM classification using Scikit Learn can be helpful for Data Science Managers who want to enhance their machine learning skills.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They use linear SVM classification and other techniques to solve real-world problems. This course on linear SVM classification using Scikit Learn provides a valuable overview of this technique and can help Machine Learning Engineers enhance their skills.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They use linear SVM classification and other techniques to solve challenging problems in a variety of fields. This course on linear SVM classification using Scikit Learn can be helpful for Machine Learning Researchers who want to expand their knowledge of this technique.
Data Scientist
Data Scientist
Data Scientists analyze data from multiple sources and transform data into actionable insights. They use machine learning models, such as linear SVM classification, to predict outcomes and identify patterns. Taking this course on linear SVM classification using Scikit Learn can provide a strong foundation for a career as a Data Scientist, as it teaches the fundamentals of this important machine learning technique.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and deploy artificial intelligence systems. They use linear SVM classification and other techniques to create intelligent machines that can perform a variety of tasks. This course on linear SVM classification using Scikit Learn can be helpful for Artificial Intelligence Engineers who want to enhance their machine learning skills.
Data Mining Analyst
Data Mining Analyst
Data Mining Analysts use data mining techniques to extract valuable information from large datasets. They use linear SVM classification and other techniques to identify patterns and trends in data. This course on linear SVM classification using Scikit Learn can be useful for Data Mining Analysts who want to enhance their machine learning skills.
Machine Learning Product Manager
Machine Learning Product Manager
Machine Learning Product Managers manage the development and launch of machine learning products. They use linear SVM classification and other techniques to ensure that machine learning products are successful. This course on linear SVM classification using Scikit Learn can be helpful for Machine Learning Product Managers who want to enhance their machine learning skills.
Quantitative Trader
Quantitative Trader
Quantitative Traders use mathematical and statistical models to trade financial assets. They use linear SVM classification and other techniques to predict financial trends and make trading decisions. This course on linear SVM classification using Scikit Learn can be useful for Quantitative Traders who want to enhance their machine learning skills.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use linear SVM classification and other statistical methods to draw meaningful conclusions from data. This course on linear SVM classification using Scikit Learn can be useful for Data Analysts who want to expand their knowledge of machine learning techniques.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use linear SVM classification and other statistical methods to draw meaningful conclusions from data. This course on linear SVM classification using Scikit Learn can be helpful for Statisticians who want to expand their knowledge of machine learning techniques.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They use linear SVM classification and other techniques to predict financial trends and make investment decisions. This course on linear SVM classification using Scikit Learn can be useful for Quantitative Analysts who want to enhance their machine learning skills.
Risk Manager
Risk Manager
Risk Managers use statistical and mathematical models to assess and manage risk. They use linear SVM classification and other techniques to identify and mitigate risks. This course on linear SVM classification using Scikit Learn can be useful for Risk Managers who want to enhance their machine learning skills.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex problems in business and industry. They use linear SVM classification and other techniques to optimize processes and improve efficiency. This course on linear SVM classification using Scikit Learn can be helpful for Operations Research Analysts who want to enhance their machine learning skills.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use machine learning techniques, such as linear SVM classification, to improve the performance and functionality of software. This course on linear SVM classification using Scikit Learn can be helpful for Software Engineers who want to incorporate machine learning into their work.
Business Analyst
Business Analyst
Business Analysts use data to identify problems and opportunities for businesses. They use linear SVM classification and other analytical techniques to make recommendations for improving business performance. This course on linear SVM classification using Scikit Learn can be useful for Business Analysts who want to expand their knowledge of machine learning techniques.
For more career information including salaries, visit:
OpenCourser.com/course/2r2gxm/linear
Reading list
We've selected 15 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
Linear SVM Classification(Soft Margin) -using Scikit Learn.
Provides a comprehensive overview of kernel methods for machine learning. It valuable reference for readers who want to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable reference for readers who want to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It valuable reference for readers who want to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of Gaussian processes for machine learning. It valuable reference for readers who want to learn more about the theoretical foundations of machine learning.
Save
Provides a comprehensive introduction to machine learning algorithms and their applications. It valuable reference for readers who want to learn more about the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of deep learning techniques. It valuable reference for readers who want to learn more about the theoretical foundations of deep learning.
Provides a comprehensive introduction to reinforcement learning. It valuable reference for readers who want to learn more about the theoretical foundations of reinforcement learning.
Provides a comprehensive overview of the mathematics used in machine learning. It valuable reference for readers who want to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of statistical learning techniques with a focus on sparsity. It valuable reference for readers who want to learn more about the theoretical foundations of statistical learning.
Provides a comprehensive overview of convex optimization techniques. It valuable reference for readers who want to learn more about the theoretical foundations of convex optimization.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It valuable reference for readers who want to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning techniques. It valuable reference for readers who want to learn more about the practical applications of machine learning.
Provides a comprehensive overview of natural language processing techniques. It valuable reference for readers who want to learn more about the practical applications of natural language processing.
Save
Provides a comprehensive overview of computer vision techniques. It valuable reference for readers who want to learn more about the practical applications of computer vision.
Provides a gentle introduction to machine learning concepts and algorithms. It good choice for readers who are new to machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2r2gxm/linear
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ashish Dikshit
Language
English
Good to know
Focuses on a crucial machine learning approach, 'Support Vector Machines', providing beneficial understanding for data classification tasks in real-world scenarios
Utilizes a hands-on approach by providing interactive materials like Python code and scientific libraries, allowing learners to actively engage with the concepts
Suitable for learners with basic knowledge of Python and data science concepts
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