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Ashish Dikshit
Non Linear SVM Classification -using SCKIT learn
Register for this course and see more details by visiting:
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Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches skills relevant to industry by using Scikit Learn
Builds a solid foundation for beginners
Helps learners develop professional skills and expertise
Ashish Dikshit is a recognized instructor
Provides a comprehensive study of non-linear SVM
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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 Non Linear SVM Classification -using SCKIT learn with these
activities:
Review support vector machine concepts
Show steps
Strengthen the foundational understanding of support vector machines before the course begins.
Browse courses on
Machine Learning
Show steps
-
Read through online resources and textbooks
-
Go over lecture notes and materials from previous courses
-
Complete practice problems and exercises
-
Participate in online forums and communities
Follow along with online tutorials
Show steps
Reinforce understanding of specific concepts covered in the course.
Show steps
-
Search for relevant tutorials on platforms like YouTube and Coursera
-
Follow along with the tutorials and take notes
-
Complete any exercises or quizzes provided in the tutorials
Join or start a study group
Show steps
Collaborate with peers to discuss concepts, solve problems, and exchange insights.
Show steps
-
Find or create a study group with fellow students taking the course
-
Meet regularly to discuss course material
-
Work together on assignments and projects
-
Support and motivate each other
Two other activities
Expand to see all activities and additional details
Show all five activities
Participate in Kaggle competitions
Show steps
Apply SVM techniques in real-world scenarios and compete with others to enhance skills.
Browse courses on
Data Analysis
Show steps
-
Identify relevant Kaggle competitions that involve non-linear SVM classification
-
Prepare data, build and tune SVM models
-
Submit solutions and track progress
-
Analyze results and learn from feedback
Contribute to open-source SVM projects
Show steps
Deepen understanding and gain practical experience in SVM implementation.
Show steps
-
Identify open-source SVM projects on platforms like GitHub
-
Review the codebase and documentation
-
Propose and implement improvements or new features
-
Collaborate with the project maintainers
Review support vector machine concepts
Show steps
Strengthen the foundational understanding of support vector machines before the course begins.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read through online resources and textbooks
- Go over lecture notes and materials from previous courses
- Complete practice problems and exercises
- Participate in online forums and communities
Follow along with online tutorials
Show steps
Reinforce understanding of specific concepts covered in the course.
Show steps
Show steps
Show steps
- Search for relevant tutorials on platforms like YouTube and Coursera
- Follow along with the tutorials and take notes
- Complete any exercises or quizzes provided in the tutorials
Join or start a study group
Show steps
Collaborate with peers to discuss concepts, solve problems, and exchange insights.
Show steps
Show steps
Show steps
- Find or create a study group with fellow students taking the course
- Meet regularly to discuss course material
- Work together on assignments and projects
- Support and motivate each other
Two other activities
Expand to see all activities and additional details
Show all five activities
Participate in Kaggle competitions
Show steps
Apply SVM techniques in real-world scenarios and compete with others to enhance skills.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify relevant Kaggle competitions that involve non-linear SVM classification
- Prepare data, build and tune SVM models
- Submit solutions and track progress
- Analyze results and learn from feedback
Contribute to open-source SVM projects
Show steps
Deepen understanding and gain practical experience in SVM implementation.
Show steps
Show steps
Show steps
- Identify open-source SVM projects on platforms like GitHub
- Review the codebase and documentation
- Propose and implement improvements or new features
- Collaborate with the project maintainers
Career center
Learners who complete Non Linear SVM Classification -using SCKIT learn will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists use data to solve business problems. They use a variety of techniques to analyze data, including machine learning, statistics, and data mining. The Non Linear SVM Classification course can help data scientists build a foundation in machine learning, which is a key skill for data scientists. This course can also help data scientists understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers build and deploy machine learning models. They use a variety of techniques to develop and evaluate machine learning models, including machine learning, statistics, and data engineering. The Non Linear SVM Classification course can help machine learning engineers build a foundation in machine learning, which is a key skill for machine learning engineers. This course can also help machine learning engineers understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Data Analyst
Data Analyst
Data analysts use data to solve business problems. They use a variety of techniques to analyze data, including machine learning, statistics, and data mining. The Non Linear SVM Classification course can help data analysts build a foundation in machine learning, which can be used to develop more sophisticated and accurate data analysis models. This course can also help data analysts understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use advanced analytical techniques to help businesses make better decisions. They use mathematical models to analyze data and identify patterns that can help businesses improve their operations. The Non Linear SVM Classification course can help operations research analysts build a foundation in the use of machine learning techniques, which can be used to develop more accurate and sophisticated models. This course can also help operations research analysts understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Marketing Manager
Marketing Manager
Marketing managers are responsible for the development and implementation of marketing campaigns. They work with a variety of stakeholders, including product managers, sales professionals, and public relations professionals, to promote products and services to target audiences. The Non Linear SVM Classification course can help marketing managers build a foundation in machine learning, which can be used to develop more targeted and effective marketing campaigns. This course can also help marketing managers understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Sales Manager
Sales Manager
Sales managers are responsible for the development and implementation of sales strategies. They work with a variety of stakeholders, including sales professionals, marketing professionals, and customer service professionals, to sell products and services to target audiences. The Non Linear SVM Classification course can help sales managers build a foundation in machine learning, which can be used to develop more effective sales strategies. This course can also help sales managers understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical models to analyze financial data. They use these models to make investment decisions and to manage risk. The Non Linear SVM Classification course can help quantitative analysts build a foundation in machine learning, which can be used to develop more sophisticated and accurate financial models. This course can also help quantitative analysts understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Customer Success Manager
Customer Success Manager
Customer success managers are responsible for the success of customers using a company's products and services. They work with customers to ensure that they are satisfied with their products and services and that they are using them to their full potential. The Non Linear SVM Classification course can help customer success managers build a foundation in machine learning, which can be used to develop more effective customer success strategies. This course can also help customer success managers understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Market Researcher
Market Researcher
Market researchers use data to understand consumer behavior. They use a variety of techniques to collect and analyze data, including surveys, focus groups, and data mining. The Non Linear SVM Classification course can help market researchers build a foundation in machine learning, which can be used to develop more sophisticated and accurate market research models. This course can also help market researchers understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
User Experience Researcher
User Experience Researcher
User experience researchers use research methods to understand how users interact with products and services. They use a variety of techniques to collect and analyze data, including surveys, interviews, and usability testing. The Non Linear SVM Classification course can help user experience researchers build a foundation in machine learning, which can be used to develop more sophisticated and accurate user experience research models. This course can also help user experience researchers understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Financial Analyst
Financial Analyst
Financial analysts use financial data to make investment decisions. They use a variety of techniques to analyze financial data, including financial modeling, statistics, and data mining. The Non Linear SVM Classification course can help financial analysts build a foundation in machine learning, which can be used to develop more sophisticated and accurate financial models. This course can also help financial analysts understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software applications. They use a variety of programming languages and technologies to develop software applications. The Non Linear SVM Classification course can help software engineers build a foundation in machine learning, which can be used to develop more intelligent and sophisticated software applications. This course can also help software engineers understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Business Analyst
Business Analyst
Business analysts use data to solve business problems. They use a variety of techniques to analyze data, including machine learning, statistics, and data mining. The Non Linear SVM Classification course can help business analysts build a foundation in machine learning, which can be used to develop more sophisticated and accurate business models. This course can also help business analysts understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Product Manager
Product Manager
Product managers are responsible for the development and launch of new products and services. They work with a variety of stakeholders, including engineers, designers, and marketing professionals, to bring new products to market. The Non Linear SVM Classification course can help product managers build a foundation in machine learning, which can be used to develop more innovative and successful products. This course can also help product managers understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. They use statistical methods to make inferences about the world around us. The Non Linear SVM Classification course can help statisticians build a foundation in machine learning, which can be used to develop more sophisticated and accurate statistical models. This course can also help statisticians understand the different types of machine learning algorithms and how to select the best algorithm for a given problem.
For more career information including salaries, visit:
OpenCourser.com/course/8cnjmm/non
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
Non Linear SVM Classification -using SCKIT learn.
Save
Provides a comprehensive overview of deep learning, including the theoretical foundations, algorithms, and applications. It valuable resource for researchers and practitioners interested in understanding and using deep learning.
Provides a comprehensive overview of statistical learning. It covers a wide range of topics, including supervised and unsupervised learning, as well as deep learning.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised and unsupervised learning, as well as deep learning.
Provides a probabilistic perspective on machine learning. It covers a wide range of machine learning topics, including Bayesian inference, graphical models, and reinforcement learning.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It covers a wide range of topics, including probability theory, Bayesian inference, and graphical models.
Provides a comprehensive overview of kernel methods, which are a powerful tool for machine learning. It covers the theoretical foundations of kernel methods, as well as their applications to a variety of machine learning tasks, including classification, regression, and clustering.
Provides a practical guide to machine learning for hackers. It covers a wide range of machine learning topics, including data preprocessing, model training, and evaluation.
Provides a practical guide to machine learning using Python and the Scikit-Learn, Keras, and TensorFlow libraries. It covers a wide range of machine learning topics, including data preprocessing, model training, and evaluation.
Provides a practical guide to machine learning using R. It covers a wide range of machine learning topics, including data preprocessing, model training, and evaluation.
Provides a practical guide to machine learning using Python. It covers a wide range of machine learning topics, including data preprocessing, model training, and evaluation.
Provides a practical guide to deep learning using Python. It covers a wide range of deep learning topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a gentle introduction to machine learning. It covers the basics of supervised and unsupervised learning, as well as deep learning.
Provides a gentle introduction to machine learning. It covers the basics of supervised and unsupervised learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8cnjmm/non
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Ashish Dikshit
Good to know
Teaches skills relevant to industry by using Scikit Learn
Builds a solid foundation for beginners
Helps learners develop professional skills and expertise
Ashish Dikshit is a recognized instructor
Provides a comprehensive study of non-linear SVM
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