AI Lesson 28 – Support Vector Machines(SVM) | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Support Vector Machines (SVM)

Support Vector Machines, commonly called SVMs, are powerful supervised machine learning algorithms used mainly for classification problems. They are known for their strong mathematical foundation and their ability to handle complex decision boundaries.

In this lesson, you will understand how SVM works, why it is effective, where it is used in real-world applications, and how to implement it using code.

Why Support Vector Machines?

Some classification problems cannot be solved well with simple linear boundaries. Data points may overlap or form complex patterns.

SVM is designed to find the best possible boundary that separates classes with maximum margin.

  • Works well with high-dimensional data
  • Effective even when data is not perfectly separable
  • Strong theoretical guarantees

What Is an SVM?

An SVM is a classifier that finds a hyperplane that best separates data points into different classes.

The goal is not just to separate classes, but to do so in a way that maximizes the distance between the nearest points of each class and the boundary.

Real-World Example

Imagine a bank deciding whether to approve or reject a loan. Customer data points belong to two classes: approved and rejected.

SVM finds the safest boundary that separates these two groups so future decisions are made with higher confidence.

Key Concepts in SVM

  • Hyperplane: The decision boundary separating classes
  • Margin: Distance between the hyperplane and nearest data points
  • Support Vectors: Data points closest to the boundary

Only support vectors influence the position of the boundary, which makes SVM efficient.

Linear vs Non-Linear SVM

When data can be separated using a straight line, linear SVM is used.

For complex patterns, SVM uses the kernel trick to transform data into higher dimensions where separation becomes possible.

Common Kernels

  • Linear Kernel
  • Polynomial Kernel
  • Radial Basis Function (RBF)
  • Sigmoid Kernel

Simple SVM Example (Conceptual)

The following code demonstrates how an SVM classifier can be trained using Python. 


from sklearn import svm

# Sample data
X = [[1,2],[2,3],[3,3],[6,5],[7,7],[8,6]]
y = [0,0,0,1,1,1]

# Create SVM model
model = svm.SVC(kernel='linear')

# Train model
model.fit(X, y)

# Predict
prediction = model.predict([[4,4]])
print(prediction)
[1]

The model predicts that the new data point belongs to class 1. This decision is based on the optimal boundary learned from the data.

How SVM Makes Decisions

SVM evaluates where a new data point lies relative to the decision boundary.

If the point lies on one side, it belongs to class 0; if on the other side, it belongs to class 1.

Advantages of SVM

  • Effective in high-dimensional spaces
  • Robust against overfitting
  • Works well with small datasets

Limitations of SVM

  • Not suitable for very large datasets
  • Kernel selection can be tricky
  • Less interpretable compared to simpler models

Practice Questions

Practice 1: Which data points define the SVM boundary?



Practice 2: What does SVM try to maximize?



Practice 3: What technique allows SVM to handle non-linear data?



Quick Quiz

Quiz 1: What is the SVM decision boundary called?





Quiz 2: Which kernel is commonly used for non-linear SVM?





Quiz 3: SVM is mainly used for?





Coming up next: Decision Trees — intuitive models that make decisions using tree-like rules.

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