Support vector machine learning.pptx
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Support vector machines (SVMs) are classifiers that output an optimal separating hyperplane to categorize data points. SVMs can handle both linearly and non-linearly separable data using techniques like kernels, regularization, tuning the gamma and margin parameters. Key aspects of SVMs include maximizing the margin distance between the hyperplane and closest data points (support vectors), using kernels to transform data non-linearly, and adjusting regularization (C parameter) to control overfitting/underfitting.
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What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Why learn Machine Learning?
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Why learn Machine Learning?
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Who should take this Machine Learning Training Course?
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3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
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Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
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- 1. Support Vector Machine • Support Vector Machine (SVM) is a discriminative classifier which intakes training data (supervised learning), the algorithm outputs an optimal hyper plane which categorizes new examples. What could be drawn to classify the black dots from blue squares?
- 2. Support Vector Machine • Linearly Separable Data A Line drawn between these data point classify the black dots and blue squares.
- 3. Linearly Vs non – linearly Separable Data Linearly separable data Non Linearly separable data
- 4. Non linearly Separable Data • What could be drawn to classify these data points (red dots from blue stars?
- 5. Non linearly Separable Data • Here the hyperplane is a 2d plane drawn parallel to x-axis that is the separator.
- 6. Non linear Data Type (2) Raw Data Line as hyperplane
- 7. For the previous data the line , if used as a Hyperplane Two black dots also fall in category of blue squares Data separation is not perfect It tolerates some outliers in the classification Non linear Data Type (2)
- 8. • This type of separator best provides the classification. But ● It is quite difficult to train a model like this . ● This is termed as Regularization parameter. Best Approach
- 9. Kernel Regularization Gamma Margin Tuning Parameters Parameter That can help to solve the difficulties in separating data.
- 10. Margin Margin is the perpendicular distance between the closest data points and the Hyperplane ( on both sides ) The best optimised line ( hyperplane ) with maximum margin is termed as Margin Maximal Hyperplane. The closest points where the margin distance is calculated are considered as the support vectors.
- 11. Margin
- 12. Regularization Also the ‘ C ‘ parameter in Python’s SkLearn Library ● Optimises SVM classifier to avoid misclassifying the data. ● C → large Margin of hyperplane → small ● C → small Margin of hyperplane → large ● misclassification(possible) 1. C ---> large , chance of overfit 2. 2. C ---> small , chance of underfitting
- 13. Regularization
- 14. Gamma Defines how far influences the calculation of of plausible line of separation. ● Low gamma -----> points far from plausible line are considered for calculation ● High gamma -----> points close to plausible line are considered for calculation
- 15. Gamma
- 16. Kernel Mathematical functions for transforming data ● using some linear algebra ● Different SVM algorithms use different types of kernel functions
- 17. Various Kernel • 1. Linear kernel • 2. Non - linear kernel • 3. Radial basis function ( RBF ) • 4. Sigmoid • 5. Polynomial • 6. Exponential
- 18. Various Kernel Example : K(x, y) = <f(x).f(y)> Kernel function dot product of n- dimensional inputs
- 19. Various Kernel
- 20. Pros
- 21. Cons ● It doesn’t perform well, when we have large data set because the required training time is higher ● It also doesn’t perform very well, when the data set has more noise i.e. target classes are overlapping ● SVM doesn’t directly provide probability estimates, these are calculated using an expensive five-fold cross-validation. It is related SVC method of Python scikit-learn library.