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Top 8 Best Support Vector Machines (SVMs)

Looking for Support Vector Machines (SVMs)? We have made a list from the very best choice. Go ahead and find out their features.

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Support Vector Machines (SVMs) are supervised learning models used for classification and regression tasks. SVMs use a hyperplane to separate data into two classes, and then optimize the hyperplane to maximize the margin between the two classes. SVMs are effective in high dimensional spaces and can be used for non-linear classification. They are also memory efficient and can be used for large datasets.
  • Towards Data Science
    Towards Data Science

    Towards Data Science - Data science content for all levels.

    Data Analysis

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  • KDNuggets
    KDNuggets

    KDNuggets - Data science resource for professionals.

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  • Analytics India Magazine
    Analytics India Magazine

    Analytics India Magazine - Data-driven insights for the Indian analytics community.

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  • Machine Learning Mastery
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    Machine Learning Mastery - A comprehensive machine learning resource.

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    Medium

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Top 10 Sites for the Support Vector Machines (SVMs)

1.

Towards Data Science

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Towards Data Science is a Medium publication that focuses on data science, sharing concepts, ideas, and codes. It is a platform for data scientists, machine learning engineers, and other professionals to share their knowledge and insights with the wider community. Towards Data Science is part of the Medium ecosystem, but there will always be free articles available for everyone interested in data science. The publication covers a wide range of topics related to data science, including machine learning, programming, data visualization, and artificial intelligence. It also provides tips for presenting data science ideas to business leaders and discusses how data science can improve startups. Towards Data Science aims to help readers learn more about data science and stay up-to-date with the latest trends and developments in the field.

Pros

  • pros Accessible Knowledge Base
  • pros Quality Content Providers
  • pros Comprehensive Resources

Cons

  • consLack of moderation
  • consUnverified content
  • consAd-heavy site

2.

KDNuggets

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KDnuggets is a leading website that focuses on data science, machine learning, AI, and analytics. It provides a wide range of resources, including articles, tutorials, news, and discussions, to help professionals stay informed about the latest developments in these fields. The website covers various topics, such as machine learning, deep learning, data visualization, and data tools. KDnuggets also offers a newsletter and forums to foster a community of data scientists and professionals. The site was founded by Gregory Piatetsky-Shapiro, a well-known figure in the field of data science. KDnuggets has a large audience, with over 1 million unique monthly visitors and more than 360,000 subscribers. It accepts advertising relevant to data science, machine learning, AI, and analytics products or services. Overall, KDnuggets serves as a valuable resource for individuals interested in staying up-to-date with the latest trends and advancements in data science and related fields.

3.

Analytics India Magazine

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Analytics India Magazine is a digital media platform that covers news, analysis, and insights on the Indian analytics, data science, and big data industries. The platform provides information on the latest trends, technologies, and tools in the analytics industry, as well as interviews with industry leaders and experts. Analytics India Magazine also offers resources such as webinars, whitepapers, and events to help professionals stay up-to-date with the latest developments in the field. The business of Analytics India Magazine is to provide information and resources to professionals in the Indian analytics industry.

4.

Machine Learning Mastery

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Machinelearningmastery.com is a website founded by Jason Brownlee, PhD, that aims to help developers accelerate their understanding and application of machine learning. The website provides tutorials, articles, and resources on machine learning concepts, algorithms, and techniques. It focuses on practical, hands-on approaches to machine learning, providing developers with the knowledge and tools to get better results faster. The content on Machine Learning Mastery emphasizes the use of working code and practical examples, making it accessible to developers with varying levels of experience in machine learning. The website also offers ebooks and courses on machine learning with Python, catering to developers interested in making accurate predictions and delivering results using the Python ecosystem.

5.

Data Science Central

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DataScienceCentral.com is a website that provides news and analysis on big data. It is a community of data scientists, analysts, and other professionals who share their knowledge and expertise in the field of data science. The website offers a variety of resources, including articles, webinars, and tutorials, to help individuals and organizations stay up-to-date on the latest trends and best practices in data science. The website covers a wide range of topics related to data science, including data visualization, data management, and data consulting. Overall, DataScienceCentral.com is a valuable resource for anyone interested in the field of data science and looking to stay informed on the latest developments in the industry.

Pros

  • pros Comprehensive data science resources
  • pros Active and engaged community
  • pros Diverse range of topics

Cons

  • consLimited industry-specific content
  • consOverwhelming amount of information

6.

Analytics Vidhya

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Analytics Vidhya is an online platform that provides training and resources for data science and analytics professionals. The website offers a variety of courses, tutorials, and articles on topics such as machine learning, data visualization, and big data. Analytics Vidhya also hosts hackathons and competitions to help data scientists improve their skills and showcase their expertise. In addition, the website provides a job board and a community forum where users can connect with other professionals in the field. Overall, Analytics Vidhya aims to help individuals and organizations stay up-to-date with the latest trends and developments in data science and analytics.

7.

Scikit-Learn

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Scikit-learn is an open-source Python library that provides simple and efficient tools for predictive data analysis. It is built on NumPy, SciPy, and Matplotlib, and is accessible to everyone, and reusable in various contexts. Scikit-learn is the gold standard for machine learning (ML) in the Python ecosystem. It includes a variety of unsupervised and supervised learning techniques, such as model selection, classification, preprocessing, clustering, and regression. Scikit-learn is versatile and integrates well with other Python libraries, such as Matplotlib, NumPy, and Pandas. It is also well-documented and easy to learn and use. Scikit-learn is a central tool in the wider ecosystem of data-science tools based on the Python programming language. It is used in various fields, such as business intelligence, health, industrial processes, and scientific discoveries.

Pros

  • pros Simple and efficient tools for predictive data analysis
  • pros Accessible to everybody, and reusable in various contexts
  • pros Built on NumPy, SciPy, and matplotlib
  • pros Open source, commercially usable - BSD license

Cons

  • consnot support deep learning
  • consdifficult to use for complex tasks

8.

Medium

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Medium is an American online publishing platform that was developed by Evan Williams and launched in August 2012. It is owned by A Medium Corporation and is an example of social journalism, having a hybrid collection of amateur and professional people and publications, or exclusive blogs or publishers on Medium, and is regularly regarded as a blog host. Medium is a publishing platform estimated to receive over 200 million visits per month. The content you publish on Medium has the opportunity to tap into that monthly traffic and grow an audience. Publishing content on Medium enables you to leverage Medium’s reach in order to build email newsletter subscribers and traffic to the company site. Medium is a CMS without a million different plugins and add-ons, making the publishing experience fast and easy. Medium has brought in revenue through native advertising and sponsorship of some article series. Medium users tend to be tech-savvy, well-educated and interested in current events and personal growth. Medium is an ideal platform for businesses that want to connect with their target audience through long-form content and storytelling.

Pros

  • pros Easy to use
  • pros Informative
  • pros Engaging

Cons

  • consLack of control
  • consNo ownership
  • consNo support

Frequently Asked Questions About Support Vector Machines (SVMs)

less What are the advantages of using SVMs?

1. SVMs are effective in high dimensional spaces, even when the number of dimensions is greater than the number of samples. 2. SVMs are memory efficient, using a subset of training points in the decision function, called support vectors. 3. SVMs are versatile, as different Kernel functions can be specified for the decision function. Common kernels are linear, polynomial, radial basis function (RBF), and sigmoid. 4. SVMs provide a good out-of-sample generalization, if the parameters are chosen correctly. 5. SVMs are robust against overfitting, as they use regularization technique. 6. SVMs are less prone to overfitting, as they use the kernel trick to implicitly map the data into a higher dimensional space.

less How do SVMs handle non-linear data?

Support Vector Machines (SVMs) are a type of supervised machine learning algorithm that can be used to classify data that is not linearly separable. SVMs use a technique called the kernel trick to transform the data into a higher dimensional space, where it can be separated by a hyperplane. This transformation allows the SVM to classify data that is not linearly separable in the original space.

less What is the difference between a linear and a non-linear SVM?

A linear SVM is a type of Support Vector Machine (SVM) that uses a linear kernel to separate data points. It is used for binary classification problems, where the goal is to separate data points into two distinct classes. The linear SVM finds the best hyperplane that separates the two classes, and then uses that hyperplane to classify new data points. A non-linear SVM is a type of Support Vector Machine (SVM) that uses a non-linear kernel to separate data points. It is used for more complex classification problems, where the goal is to separate data points into multiple distinct classes. The non-linear SVM finds the best non-linear hyperplane that separates the classes, and then uses that hyperplane to classify new data points.

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