AI Course Index – Dataplexa
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AI Course Index

Master Artificial Intelligence with 108 complete, structured lessons. Learn AI theory, neural networks, transformers, NLP, computer vision, LLM engineering and real-world deployment.

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I. AI Foundations (Lessons 1–20)

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 Foundations (Lessons 21–40)

21. Introduction to Machine Learning 22. ML Workflow & Pipelines 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. Support Vector Machines (SVM) 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost Complete Guide 33. K-Means Clustering 34. Hierarchical Clustering 35. Dimensionality Reduction (PCA, LDA) 36. Feature Engineering for AI 37. Feature Selection Techniques 38. Model Evaluation Metrics 39. Overfitting & Underfitting 40. Cross-Validation Methods

III. Deep Learning & Neural Networks (Lessons 41–60)

41. Introduction to Deep Learning 42. Neural Network Basics 43. Activation Functions 44. Forward Pass & Backpropagation 45. Loss Functions & Optimizers 46. Regularization & Dropout 47. CNN Basics (Convolutional Nets) 48. Popular CNN Architectures (VGG, ResNet) 49. RNN, LSTM & GRU 50. Sequence-to-Sequence Models 51. Transformers – Introduction 52. Attention Mechanism Explained 53. Positional Encoding 54. Training Deep Networks Efficiently 55. Hyperparameter Tuning in DL 56. Transfer Learning & Fine-Tuning 57. Autoencoders & Applications 58. GAN Basics (Generative Adversarial Nets) 59. GAN Applications (Images, Media) 60. Serving Deep Learning Models in Production

IV. Natural Language Processing (Lessons 61–80)

61. Introduction to NLP 62. Text Processing & Normalization 63. Tokenization Techniques 64. Stopwords, Stemming & Lemmatization 65. Bag of Words (BoW) & TF-IDF 66. Word Embeddings (Word2Vec, GloVe) 67. Sentence Embeddings (SBERT) 68. NLP Classification Models 69. Sequence Modeling in NLP 70. RNN, LSTM & GRU in NLP 71. Transformers in NLP 72. BERT – Basics & Architecture 73. Fine-Tuning BERT 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. Named Entity Recognition (NER) 77. Machine Translation (NMT) 78. Text Generation Models 79. NLP Evaluation Metrics 80. NLP Use Cases & Applications

V. Computer Vision (Lessons 81–95)

81. Introduction to Computer Vision 82. Image Processing Basics 83. Filters & Edge Detection 84. Convolutions & Kernels 85. Object Detection (YOLO, SSD) 86. Object Tracking 87. Image Segmentation (U-Net) 88. Face Detection & Recognition 89. Feature Detection (SIFT, ORB) 90. OCR & Text Extraction 91. Image Augmentation Techniques 92. Generative Vision Models 93. Vision Transformers (ViT) 94. Multimodal Models 95. CV Use Cases & Applications

VI. LLM Engineering & Advanced AI (Lessons 96–108)

96. Introduction to LLMs 97. Tokenization for LLMs 98. LLM Architecture Explained 99. Pretraining LLMs 100. Finetuning LLMs 101. RLHF (Reinforcement Learning from Human Feedback) 102. Prompt Engineering (Advanced) 103. Embedding Models 104. Vector Databases (FAISS, Pinecone) 105. LLM Agents & Autonomous Systems 106. Guardrails, Safety & Alignment 107. AI Systems in Production 108. End-to-End AI Project