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CampusEduX
Free course

AI/ML Notes

Complete AI & Machine Learning course — Python and math prerequisites, ML fundamentals, data preprocessing, supervised and unsupervised learning, model evaluation and tuning, the essential ML libraries, hands-on practical projects, and a foundational tour of deep learning, computer vision, NLP and generative AI. Every topic has notes, a 10-question test, and coding practice.

Sections
1 section
Lessons
77 lessons

Course content

1 section · 77 lessons

1All Topics77 lessons
  1. 1Python Basics
  2. 2Variables, Data Types, Loops
  3. 3Functions
  4. 4OOP Basics
  5. 5NumPy
  6. 6Pandas
  7. 7Matplotlib
  8. 8Basic Statistics
  9. 9Probability Basics
  10. 10Linear Algebra Basics
  11. 11What is AI?
  12. 12What is Machine Learning?
  13. 13AI vs ML vs Deep Learning
  14. 14Types of Machine Learning
  15. 15Supervised Learning
  16. 16Unsupervised Learning
  17. 17Reinforcement Learning
  18. 18Training, Validation & Testing
  19. 19Features & Labels
  20. 20Model, Parameters & Hyperparameters
  21. 21Loading Datasets
  22. 22Data Cleaning
  23. 23Handling Missing Values
  24. 24Removing Duplicates
  25. 25Outlier Detection
  26. 26Encoding Categorical Data
  27. 27Feature Scaling
  28. 28Feature Selection
  29. 29Train-Test Split
  30. 30Linear Regression
  31. 31Multiple Linear Regression
  32. 32Logistic Regression
  33. 33K-Nearest Neighbors
  34. 34Decision Trees
  35. 35Random Forest
  36. 36Support Vector Machines
  37. 37Naive Bayes
  38. 38Clustering
  39. 39K-Means Clustering
  40. 40Hierarchical Clustering
  41. 41DBSCAN
  42. 42PCA (Principal Component Analysis)
  43. 43Accuracy, Precision, Recall, F1-Score
  44. 44Confusion Matrix
  45. 45ROC-AUC Curve
  46. 46Cross-Validation
  47. 47Regression Metrics (MAE, MSE, RMSE, R²)
  48. 48Bias-Variance Tradeoff
  49. 49Overfitting & Underfitting
  50. 50Regularization (L1/L2)
  51. 51Hyperparameter Tuning
  52. 52Feature Engineering
  53. 53Ensemble Techniques
  54. 54Scikit-learn
  55. 55NumPy (Condensed Reference)
  56. 56Pandas (Condensed Reference)
  57. 57Matplotlib (Condensed Reference)
  58. 58Seaborn
  59. 59Jupyter Notebook
  60. 60House Price Prediction
  61. 61Student Score Prediction
  62. 62Spam Email Detection
  63. 63Customer Churn Prediction
  64. 64Movie Recommendation System
  65. 65Customer Segmentation
  66. 66Disease Prediction
  67. 67Sentiment Analysis
  68. 68Deep Learning Basics
  69. 69Neural Networks
  70. 70TensorFlow / Keras
  71. 71PyTorch
  72. 72Computer Vision
  73. 73NLP (Natural Language Processing)
  74. 74Generative AI
  75. 75LLM Basics
  76. 76Model Deployment
  77. 77MLOps Basics