Support Vector Machines (SVM) are supervised machine learning algorithms used for classification and regression. They work by finding the optimal hyperplane that separates data into distinct classes. SVM handles both linear and non-linear data using techniques like the kernel trick.

1. Understanding
Support Vector
Machines
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2. What is a Support Vector
Machine?
Supervised Learning
SVM is a supervised machine
learning algorithm.
Classification and
Regression
Used for both classification
and regression tasks.
Optimal Boundary
Finds the best boundary (hyperplane) to separate data.
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3. Key Concepts
Hyperplane
Decision boundary
separating classes.
Margin
Distance between
hyperplane and support
vectors.
Support Vectors
Data points defining the
hyperplane.
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4. How SVM Works
Data Representation
Represent data in higher-dimensional space.
Margin Maximization
Maximize the margin between data points.
Support Vectors
Identify support vectors influencing the hyperplane.
Classification
Classify new data based on hyperplane position.
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5. Linear vs Non-Linear SVM
Linear SVM: Used when data is linearly separable (a straight line can
separate classes).
Non-Linear SVM: Used for data that isn't linearly separable; utilizes
the kernel trick to transform data into higher dimensions.
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6. The Kernel Trick
Mapping to Higher
Dimensions
Eases separation with a
hyperplane.
Advantages
Avoids explicitly
transforming data.
Common Kernels
Linear, Polynomial, RBF.
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7. Advantages of SVM
Effective in high-dimensional spaces.
Robust to overfitting, especially with a clear margin of separation.
Works well with both linear and non-linear data using the kernel
trick.
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8. Limitations of SVM
Computationally
Expensive
Especially with large datasets.
Kernel Selection
Performance depends on
kernel selection.
Not Ideal for Large Datasets
Inefficient with very large datasets.
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9. Applications of SVM
Text Classification
Spam detection and
sentiment analysis.
Image Recognition
Handwriting and facial
recognition.
Bioinformatics
Cancer detection and
gene expression.
Speech Recognition
Recognizing audio
patterns.
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10. Certification
Enhance Skills
Mastering SVM improves
skills.
Consider Certification
Gain a competitive edge.
IABAC Certification
Boosts career prospects.
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