AI Lesson 52 – Positional Encoding | 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

K-Means Clustering

K-Means is an unsupervised machine learning algorithm used to group data into clusters. Unlike supervised learning, K-Means does not use labeled data. Instead, it finds patterns and similarities within the data and groups similar points together.

The goal of K-Means is simple: data points within the same cluster should be as similar as possible, while data points in different clusters should be as different as possible.

Real-World Connection

Imagine an online shopping website grouping customers based on their buying behavior. Customers who buy similar products are grouped together. These clusters help businesses design personalized offers and recommendations. K-Means performs this grouping automatically using data.

Where K-Means Is Used

  • Customer segmentation
  • Market research
  • Image segmentation
  • Document clustering
  • Recommendation systems

How K-Means Works

The algorithm follows an iterative process:

  • Select the number of clusters (K)
  • Randomly initialize K centroids
  • Assign each data point to the nearest centroid
  • Recalculate centroids based on assignments
  • Repeat until centroids stop changing

K-Means Example Using Python


from sklearn.cluster import KMeans
import numpy as np

X = np.array([
    [1, 2],
    [1, 4],
    [1, 0],
    [10, 2],
    [10, 4],
    [10, 0]
])

kmeans = KMeans(n_clusters=2, random_state=42)
kmeans.fit(X)

print(kmeans.labels_)
print(kmeans.cluster_centers_)
[0 0 0 1 1 1] [[ 1. 2.] [10. 2.]]

Understanding the Output

The labels show which cluster each data point belongs to. Points with similar values are grouped together. The cluster centers represent the average position of points in each cluster.

Choosing the Value of K

Selecting the correct number of clusters is important. A common technique is the Elbow Method, where we plot the error against different values of K and look for a point where improvement slows down.

Limitations of K-Means

  • You must choose K in advance
  • Works best with spherical clusters
  • Sensitive to outliers
  • Results may change based on initialization

Practice Questions

Practice 1: K-Means belongs to which type of learning?



Practice 2: What represents the center of a cluster?



Practice 3: What does K represent in K-Means?



Quick Quiz

Quiz 1: What is the main goal of K-Means?





Quiz 2: Which method helps choose the value of K?





Quiz 3: K-Means improves clusters using which process?





Coming up next: Hierarchical Clustering — building clusters step by step.

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