100 Things an AI Engineer Should Know

 

100 Things an AI Engineer Should Know

Foundations

  1. Python programming
  2. Data structures and algorithms
  3. Linear algebra
  4. Probability theory
  5. Statistics
  6. Calculus basics
  7. Optimization concepts
  8. Matrix operations
  9. Vector spaces
  10. Numerical computing

Programming & Software Engineering

  1. Object-oriented programming
  2. Functional programming basics
  3. Git and version control
  4. Linux command line
  5. Shell scripting
  6. APIs and REST
  7. JSON and YAML
  8. Debugging skills
  9. Unit testing
  10. Software design principles

Python Ecosystem

  1. NumPy
  2. Pandas
  3. Matplotlib
  4. Scikit-learn
  5. Jupyter notebooks
  6. Virtual environments
  7. Pip and package management
  8. Async programming
  9. Logging
  10. Type hints

Machine Learning Basics

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning
  4. Classification
  5. Regression
  6. Clustering
  7. Dimensionality reduction
  8. Feature engineering
  9. Cross-validation
  10. Bias-variance tradeoff

Deep Learning

  1. Neural networks
  2. Backpropagation
  3. Gradient descent
  4. CNNs
  5. RNNs
  6. LSTMs
  7. Transformers
  8. Attention mechanisms
  9. Embeddings
  10. Transfer learning

Modern AI & LLMs

  1. Large Language Models (LLMs)
  2. Prompt engineering
  3. Fine-tuning
  4. RAG (Retrieval-Augmented Generation)
  5. Vector databases
  6. Tokenization
  7. Context windows
  8. AI agents
  9. Function/tool calling
  10. Multi-modal AI

Frameworks & Libraries

  1. PyTorch
  2. TensorFlow
  3. Keras
  4. Hugging Face Transformers
  5. LangChain
  6. LlamaIndex
  7. OpenCV
  8. FastAPI
  9. Docker
  10. Kubernetes

Data Engineering

  1. SQL
  2. NoSQL databases
  3. Data pipelines
  4. ETL processes
  5. Data cleaning
  6. Data labeling
  7. Big data concepts
  8. Distributed systems
  9. Streaming data
  10. Data warehousing

Deployment & MLOps

  1. Model deployment
  2. CI/CD pipelines
  3. Model monitoring
  4. Experiment tracking
  5. GPU utilization
  6. Cloud computing
  7. API serving
  8. Scalability
  9. Latency optimization
  10. Cost optimization

AI Safety & Ethics

  1. AI bias
  2. Fairness in AI
  3. Explainability
  4. Privacy concerns
  5. Hallucination handling
  6. Prompt injection risks
  7. Security practices
  8. Responsible AI
  9. Human-in-the-loop systems
  10. Continuous learning and research

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