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NumPy

A comprehensive beginner-to-practical NumPy course for AI and data work, covering arrays, shapes, indexing, broadcasting, linear algebra, statistics, random data, performance, numerical workflows, and AI-oriented examples.

Course map
Beginner → practical: This 60-chapter course starts with the basics and progresses into AI-oriented workflows. Every chapter includes simple explanations, technical words in parentheses, real-world code, expected output, step-by-step explanation, exercises, common mistakes, and Q&A.

Complete 60-Chapter Course

  1. Chapter 1: What NumPy Is
  2. Chapter 2: Installing and Importing NumPy
  3. Chapter 3: Python Lists vs NumPy Arrays
  4. Chapter 4: Creating One-Dimensional Arrays
  5. Chapter 5: Creating Two-Dimensional Arrays
  6. Chapter 6: Array Shapes
  7. Chapter 7: Array Dimensions
  8. Chapter 8: Array Size
  9. Chapter 9: Data Types
  10. Chapter 10: Changing Data Types
  11. Chapter 11: Zeros Ones and Full Arrays
  12. Chapter 12: Ranges with arange
  13. Chapter 13: Evenly Spaced Values with linspace
  14. Chapter 14: Reshaping Arrays
  15. Chapter 15: Flattening Arrays
  16. Chapter 16: Transposing Arrays
  17. Chapter 17: Indexing Basics
  18. Chapter 18: Negative Indexing
  19. Chapter 19: Slicing Arrays
  20. Chapter 20: Boolean Indexing
  21. Chapter 21: Fancy Indexing
  22. Chapter 22: Copy vs View
  23. Chapter 23: Changing Array Values
  24. Chapter 24: Adding Arrays
  25. Chapter 25: Subtracting Arrays
  26. Chapter 26: Multiplying Arrays
  27. Chapter 27: Dividing Arrays
  28. Chapter 28: Powers and Roots
  29. Chapter 29: Rounding Numbers
  30. Chapter 30: Absolute Values
  31. Chapter 31: Aggregation with sum
  32. Chapter 32: Mean Median and Standard Deviation
  33. Chapter 33: Minimum and Maximum
  34. Chapter 34: Argmin and Argmax
  35. Chapter 35: Sorting Arrays
  36. Chapter 36: Unique Values
  37. Chapter 37: Counting Values
  38. Chapter 38: Broadcasting
  39. Chapter 39: Broadcasting Rules
  40. Chapter 40: Vectorized Operations
  41. Chapter 41: Comparisons and Masks
  42. Chapter 42: Combining Conditions
  43. Chapter 43: Where
  44. Chapter 44: Concatenating Arrays
  45. Chapter 45: Stacking Arrays
  46. Chapter 46: Splitting Arrays
  47. Chapter 47: Matrix Multiplication
  48. Chapter 48: Dot Products
  49. Chapter 49: Identity Matrices
  50. Chapter 50: Determinants Concepts
  51. Chapter 51: Inverse Matrices Concepts
  52. Chapter 52: Solving Linear Systems Concepts
  53. Chapter 53: Eigenvalues Concepts
  54. Chapter 54: Random Numbers
  55. Chapter 55: Random Sampling
  56. Chapter 56: Reproducible Randomness
  57. Chapter 57: Mini Project: Normalize Features
  58. Chapter 58: Mini Project: Similarity Calculator
  59. Chapter 59: Mini Project: Image Pixel Array
  60. Chapter 60: Capstone: AI Numerical Data Pipeline

Course features

  • Responsive top logo and main menu.
  • Chapter menu below the main header, with all 60 links on mobile and desktop.
  • Chapter search, font increase/decrease, night view, print, and go-to-top.
  • Google Translate area with RTL-safe behavior and no intentional horizontal page movement.
  • Original lessons, SEO metadata, structured data, course map, sitemap, robots file, and legal notice.