Data Science Foundations
Essential mathematical and statistical foundations for modern data science and machine learning. Deep dive into the theoretical underpinnings of LLMs, traditional ML algorithms, probability theory, and advanced optimization techniques with comprehensive proofs and implementations.
Complete Foundations Curriculum
Master data science and LLM foundations through our structured 41-article curriculum. Progress from mathematical fundamentals to cutting-edge AI concepts.
LLM & AI Foundations (33 Articles)
1. LLM Base Knowledge (10 articles)
Foundations, tokenization, activations, language models, embeddings
2. LLM Model Architecture (9 articles)
Attention mechanisms, transformers, MOE, LLAMA models, decoding
3. Training & Fine-tuning (8 articles)
Training data, workflows, LoRA, adapter tuning, optimization
4. RAG & Evaluation (6 articles)
RAG basics, agentic RAG, hallucination detection, evaluation methods
Traditional ML & Statistics (8 Articles)
5. Probability & Statistics (4 articles)
Mathematical expectations, distributions, imbalanced data, metrics
6. ML Tools & Practice (4 articles)
Pandas operations, scikit-learn, classification metrics, ML concepts
🎯 Recommended Learning Path
- 1. Start with Traditional ML for solid foundations
- 2. Move to LLM Base Knowledge
- 3. Study Model Architecture in detail
- 4. Explore Training & Fine-tuning techniques
- 5. Master RAG & Evaluation methods
💡 Study Tip: Each article includes mathematical proofs, code implementations, and interview questions. We recommend studying 2-3 articles per week for optimal retention and understanding.
🚀 Quick Start Guides
No Posts Found
No foundation posts found. Check back soon for new content!