Your learning path

Pick how deep you want to go and how much time you have. Depth decides which articles; time decides only how long it takes.

Level

Expert

Articles

146

Reading time

193h

Weeks

That is 77 weeks at 3h per week. Raising your hours spreads the same material over less time — or drop to a lower target level to study fewer articles.

Week 1
2h
1. 1
  What Is Machine Learning?
  ML Foundations 1h
2. 2
  How Data Becomes Numbers
  ML Foundations 1h
Week 2
2.3h
1. 3
  What Is a Neural Network?
  ML Foundations 1.25h
2. 4
  Weights and Biases
  ML Foundations 1h
Week 3
3h
1. 5
  The Forward Pass
  ML Foundations 1h
2. 6
  Activation Functions
  ML Foundations 1h
3. 7
  Loss Functions
  ML Foundations 1h
Week 4
2.6h
1. 8
  Gradient Descent
  ML Foundations 1.25h
2. 9
  Backpropagation
  ML Foundations 1.25h
Week 5
3h
1. 10
  Overfitting and Generalization
  ML Foundations 1h
2. 11
  Probability for AI
  ML Foundations 1h
3. 12
  Train, Validation, Test
  ML Foundations 1h
Week 6
2.8h
1. 13
  What Is a Token in AI?
  Foundations 1h
2. 14
  Classic Text Preprocessing
  Classic NLP 0.75h
3. 15
  Bag of Words and N-grams
  Classic NLP 1h
Week 7
2.8h
1. 16
  What Is an Embedding?
  Foundations 1.5h
2. 17
  word2vec: Where Word Vectors Began
  Classic NLP 1.25h
Week 8
2.6h
1. 18
  Recurrent Neural Networks
  Classic NLP 1.25h
2. 19
  LSTM and GRU: Memory That Lasts
  Classic NLP 1.25h
Week 9
2.6h
1. 20
  Sequence to Sequence and Encoder-Decoder
  Classic NLP 1.25h
2. 21
  The Origin of Attention
  Classic NLP 1.25h
Week 10
2.3h
1. 22
  Where Does the Vocabulary Come From?
  Foundations 1.25h
2. 23
  What Is an LLM?
  Foundations 1h
Week 11
2.3h
1. 24
  How Does an LLM Actually Write?
  Foundations 1.25h Visual explainer
2. 25
  Base Models vs Chat Models
  Foundations 1h
Week 12
2.8h
1. 26
  The Language Modeling Objective
  Foundations 1h
2. 27
  Logits and Softmax
  Foundations 1h
3. 28
  Perplexity: A Language Model's Intrinsic Measure
  Foundations 0.75h
Week 13
2h
1. 29
  Temperature and Top-p, Explained
  Foundations 1h Visual explainer
2. 30
  Training vs Inference
  Foundations 1h
Week 14
2.5h
1. 31
  What Happens During Pretraining?
  Foundations 1.5h
2. 32
  What Does "7B Parameters" Mean?
  Foundations 1h
Week 15
3h
1. 33
  Decoding Strategies: Greedy Search and Beam Search
  Internals 1h
2. 34
  The Geometry of Meaning
  Internals 2h Visual explainer
Week 16
2.5h
1. 35
  How Does a Transformer Work?
  Internals 2.5h Visual explainer
Week 17
2.3h
1. 36
  Encoder, Decoder, and Encoder-Decoder Architectures
  Internals 1.25h
2. 37
  The Feed-Forward Network in a Transformer
  Internals 1h
Week 18
2.5h
1. 38
  Layers, Residuals, and Depth
  Internals 1.5h
2. 39
  Normalization Layers: LayerNorm and RMSNorm
  Internals 1h
Week 19
1h
1. 40
  The Output Projection: From Hidden State to Vocabulary Logits
  Internals 1h
Week 20
3h
1. 41
  Self-Attention, Explained Visually
  Internals 3h Visual explainer
Week 21
2.8h
1. 42
  Why Transformers Won
  Classic NLP 1.25h
2. 43
  What Is a KV Cache?
  Internals 1.5h
Week 22
2h
1. 44
  Why Multiple Attention Heads?
  Internals 2h Visual explainer
Week 23
3h
1. 45
  How Does a Model Know Word Order?
  Internals 1.5h
2. 46
  Why Long Contexts Cost So Much
  Internals 1.5h
Week 24
2.6h
1. 47
  Why Do AI Models Hallucinate?
  Prompting 1.25h
2. 48
  Prompt Basics
  Prompting 1.25h
Week 25
2.5h
1. 49
  Making a Model Think Step by Step
  Prompting 1.5h Visual explainer
2. 50
  What Is a Context Window?
  Prompting 1h
Week 26
2h
1. 51
  Show, Don't Tell: Few-Shot Prompting
  Prompting 1h
2. 52
  In-Context Learning: Why Few-Shot Works
  Prompting 1h
Week 27
2.3h
1. 53
  Your Prompt Isn't Working. Now What?
  Prompting 1.25h
2. 54
  Prompt Patterns and Anti-Patterns
  Prompting 1h
Week 28
2.1h
1. 55
  Getting JSON Out of an LLM
  Prompting 1.25h
2. 56
  What Is a System Prompt?
  Prompting 0.75h
Week 29
3h
1. 57
  Why RAG Exists
  RAG 1h
2. 58
  How RAG Works, Step by Step
  RAG 2h Visual explainer
Week 30
3h
1. 59
  How to Split Documents for RAG
  RAG 1.5h
2. 60
  RAG or Fine-Tuning?
  RAG 1.5h
Week 31
2.6h
1. 61
  Why You Need a Reranker
  RAG 1.25h
2. 62
  What Is a Vector Database?
  RAG 1.25h
Week 32
2.8h
1. 63
  How Similarity Search Actually Works
  RAG 1.5h Visual explainer
2. 64
  What Is an AI Agent?
  Agents 1.25h
Week 33
2h
1. 65
  How Does Tool Calling Work?
  Agents 2h Visual explainer
Week 34
2h
1. 66
  The Agent Loop
  Agents 2h Visual explainer
Week 35
2.8h
1. 67
  Agent Architectures: From Rules to Autonomy
  Agents 1.25h
2. 68
  How Agents Fail
  Agents 1.5h
Week 36
2.8h
1. 69
  Writing Good Tool Definitions
  Agents 1.5h
2. 70
  What Is MCP?
  Agents 1.25h
Week 37
2.8h
1. 71
  Multi-Step Planning
  Agents 1.5h
2. 72
  The ReAct Pattern
  Agents 1.25h
Week 38
3h
1. 73
  Your First LLM API Call
  Application 1.5h
2. 74
  How Do You Know It Works?
  Application 1.5h
Week 39
1.8h
1. 75
  From Demo to Production
  Application 1.75h
Week 40
2.6h
1. 76
  Which Model Should You Use?
  Application 1.25h
2. 77
  Cutting Your API Bill
  Application 1.25h
Week 41
3h
1. 78
  Why Responses Stream
  Application 1h
2. 79
  The Bias-Variance Tradeoff
  Evaluation 1h
3. 80
  Why Evaluation Is Harder Than Training
  Evaluation 1h
Week 42
2.8h
1. 81
  Precision, Recall, F1, and Accuracy
  Evaluation 1h
2. 82
  The Confusion Matrix: Why Accuracy Deceives
  Evaluation 0.75h
3. 83
  Human Evaluation and Annotation Agreement
  Evaluation 1h
Week 43
2.3h
1. 84
  Regression Metrics: MSE, MAE, and R²
  Evaluation 1h
2. 85
  Retrieval and Ranking Metrics
  Evaluation 1.25h
Week 44
2h
1. 86
  ROC and AUC: Evaluation Without a Threshold
  Evaluation 1h
2. 87
  Text Generation Metrics: BLEU, ROUGE, METEOR
  Evaluation 1h
Week 45
2h
1. 88
  How Do Models See Images?
  Multimodal 2h Visual explainer
Week 46
2h
1. 89
  How Image Generation Works
  Multimodal 2h Visual explainer
Week 47
2.8h
1. 90
  Multimodal Embeddings
  Multimodal 1.5h
2. 91
  Contrastive Learning and CLIP
  Multimodal 1.25h
Week 48
2.8h
1. 92
  How Speech Recognition Works
  Multimodal 1.5h
2. 93
  How Text-to-Speech Works
  Multimodal 1.25h
Week 49
3h
1. 94
  Vision-Language Models
  Multimodal 1.5h
2. 95
  Document and OCR Understanding
  Multimodal 1.5h
Week 50
2.8h
1. 96
  How Fine-Tuning Actually Works
  Training and Adaptation 1.5h
2. 97
  Model Distillation
  Training and Adaptation 1.25h
Week 51
2.8h
1. 98
  Hyperparameter Tuning
  Training and Adaptation 1.25h
2. 99
  LoRA and Parameter-Efficient Fine-Tuning
  Training and Adaptation 1.5h
Week 52
2.3h
1. 100
  Reading Model Benchmarks
  Training and Adaptation 1.25h
2. 101
  Benchmark Contamination and Goodhart's Law
  Evaluation 1h
Week 53
2.8h
1. 102
  Optimizers and Learning Rate
  Training and Adaptation 1.25h
2. 103
  What Is Quantization?
  Training and Adaptation 1.5h
Week 54
2.8h
1. 104
  Regularization: Dropout and Weight Decay
  Training and Adaptation 1h
2. 105
  How Models Are Aligned
  Training and Adaptation 1.75h
Week 55
2.8h
1. 106
  How Reasoning Models Work
  Training and Adaptation 1.5h
2. 107
  Synthetic Data
  Training and Adaptation 1.25h
Week 56
2.8h
1. 108
  Transfer Learning: Why Pretraining Reuses
  Training and Adaptation 1.25h
2. 109
  Attention Variants: Sparse, Linear, and Sliding Window
  Advanced Architecture 1.5h
Week 57
3h
1. 110
  How Embedding Models Are Trained
  Advanced Architecture 1.5h
2. 111
  How Inference Serving Works
  Advanced Architecture 1.5h
Week 58
3h
1. 112
  How Long Context Became Possible
  Advanced Architecture 1.5h
2. 113
  Mixture of Experts
  Advanced Architecture 1.5h
Week 59
2.6h
1. 114
  Merging and Combining Models
  Advanced Architecture 1.25h
2. 115
  Speculative Decoding
  Advanced Architecture 1.25h
Week 60
3h
1. 116
  State Space Models
  Advanced Architecture 1.5h
2. 117
  Agentic RAG
  Advanced RAG 1.5h
Week 61
1.3h
1. 118
  Contextual Retrieval
  Advanced RAG 1.25h
Week 62
2h
1. 119
  GraphRAG
  Advanced RAG 2h Visual explainer
Week 63
3h
1. 120
  Hybrid Search
  Advanced RAG 1.5h Visual explainer
2. 121
  RAG over Images and Tables
  Advanced RAG 1.5h
Week 64
3h
1. 122
  Query Rewriting and Expansion
  Advanced RAG 1.5h Visual explainer
2. 123
  Evaluating RAG
  Advanced RAG 1.5h
Week 65
3h
1. 124
  Evaluating Agents
  Advanced Agents 1.5h
2. 125
  Agent Memory
  Advanced Agents 1.5h
Week 66
3h
1. 126
  Coding Agents
  Advanced Agents 1.5h
2. 127
  Computer-Use Agents
  Advanced Agents 1.5h
Week 67
2.8h
1. 128
  Context Engineering
  Advanced Agents 1.5h
2. 129
  Human-in-the-Loop Design
  Advanced Agents 1.25h
Week 68
1.8h
1. 130
  Multi-Agent Systems
  Advanced Agents 1.75h
Week 69
2.8h
1. 131
  Model Supply Chain Risk
  Security 1.25h
2. 132
  Handling PII and Sensitive Data
  Security 1.5h
Week 70
2.8h
1. 133
  AI Governance and Compliance
  Security 1.25h
2. 134
  Prompt Injection
  Security 1.5h
Week 71
2.8h
1. 135
  Jailbreaks and Guardrails
  Security 1.5h
2. 136
  Content Moderation
  Security 1.25h
Week 72
2.8h
1. 137
  Red-Teaming AI Systems
  Security 1.5h
2. 138
  Latency Optimization
  Production 1.25h
Week 73
2.8h
1. 139
  Observability for LLM Systems
  Production 1.5h
2. 140
  Cost Monitoring and Attribution
  Production 1.25h
Week 74
2.3h
1. 141
  Prompt Versioning
  Production 1h
2. 142
  A/B Testing AI Features
  Production 1.25h
Week 75
2.3h
1. 143
  Statistical Significance: Why One Comparison Deceives
  Evaluation 1h
2. 144
  Migrating Between Models
  Production 1.25h
Week 76
1.5h
1. 145
  Reliability Patterns
  Production 1.5h
Week 77
1.8h
1. 146
  Self-Hosting Models
  Production 1.75h

Your learning path

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