Overview
This lecture provides a curated roadmap of recommended resources, books, and courses for developing skills in programming, mathematics, machine learning, deep learning, LLMs, and AI engineering.
Programming & Software Engineering
- Strong programming and software engineering skills are essential for AI careers.
- Python is the dominant language for AI, but learning Rust, Java, or Go may also be valuable for backend or future roles.
- Recommended Python resources: FreeCodeCamp’s Learn Python (4-hour course), Python for Everybody Specialization, HackerRank and LeetCode for problem-solving, NeetCode for data structures, and Harvard CS50 for computer science fundamentals.
- Practice is the most important teacher—implement learned concepts through projects.
Mathematics & Statistics
- Understanding basic statistics, linear algebra, and calculus is important for deep AI expertise.
- Recommended resources: "Practical Statistics for Data Science" (applied stats), "Mathematics for Machine Learning" book, and "Mathematics for Machine Learning and Deep Learning" specialization.
- Focus on math directly relevant to AI and machine learning.
Machine Learning
- AI includes a broad spectrum; machine learning is fundamental.
- Key resources: "Hands-On Machine Learning with Scikit-learn, TensorFlow, and Keras" (comprehensive book), Andrew Ng’s Machine Learning Specialization (theory and Python practice), "The 100-Page Machine Learning Book" (overview/reference), and "The Elements of Statistical Learning" (in-depth theory).
- Project-based bootcamps like Zero to Mastery are excellent for practical experience and career preparation.
Deep Learning & Large Language Models (LLMs)
- Deep learning is the foundation behind generative AI, LLMs, and transformers.
- Recommended to learn PyTorch for deep learning (preferred over TensorFlow in research and industry).
- Courses: Deep Learning Specialization by Andrew Ng, Andrej Karpathy’s "Intro to LLMs" video, and "Neural Networks: Zero to Hero" course (builds GPT from scratch).
- Book: "Hands-On Large Language Models" by Jay Alammar (clear LLM explanations and latest content).
AI Engineering & Deployment
- AI engineers focus on deploying and productionizing existing models, not building them from scratch.
- Key books: "Practical MLOps" (machine learning deployment) and "AI Engineering" by Chip Huyen (leading resource on deploying AI/ML systems).
Key Terms & Definitions
- AI Engineer — A role focused on integrating and deploying AI models in production environments.
- LLM (Large Language Model) — Advanced AI systems that generate and understand human language, e.g., GPT.
- MLOps — Practices and tools to deploy, monitor, and maintain machine learning models in production.
Action Items / Next Steps
- Choose one programming and one math resource to start and begin practicing by building small projects.
- Review recommended books/courses per section to address learning gaps.
- Implement and practice concepts from each topic area.
- Teach or summarize new concepts in your own words.
- Consider joining communities like Zero to Mastery or seeking coaching if additional guidance is needed.