Python Programming Fundamentals
4-5 weeksSkills You'll Build
Python syntax and data structuresControl flow, functions, and modulesObject-oriented programming basicsNumPy for numerical computingVirtual environments and package management
Mathematics/Statistics Graduate → AI Engineer
Math Graduate to AI Engineer: From Equations to AI Systems
Your mathematics or statistics background gives you a significant advantage in the AI engineering field. While most aspiring AI engineers struggle to understand gradient descent, backpropagation, and probabilistic models, you already speak that language fluently. Linear algebra, calculus, and statistics form the theoretical foundation of machine learning, and you've spent years mastering them. What you need now is the engineering layer: production Python, software architecture, and the practical skills to turn mathematical concepts into deployed AI systems. This path focuses on building your programming proficiency, introducing you to modern AI development practices, and helping you leverage your quantitative strengths in real-world applications. You'll learn to implement the algorithms you understand theoretically, work with LLM APIs, build RAG systems, and create production-ready AI applications. Your mathematical intuition will help you debug models, optimize performance, and understand why certain approaches work better than others. Timeline: 5-9 months depending on your existing programming experience.
5-9 months
Prerequisites
- Strong linear algebra foundation (matrices, eigenvalues, transformations)
- Statistics and probability theory (distributions, hypothesis testing, Bayesian reasoning)
- Calculus proficiency (derivatives, gradients, optimization concepts)
- Basic programming exposure (any language, even MATLAB or R)
- Analytical and abstract thinking skills
- Mathematical proof writing and logical reasoning
Your Learning Path
Software Engineering Essentials
3-4 weeksSkills You'll Build
Git version control and GitHub workflowCode organization and project structureUnit testing with pytestType hints and documentationCommand line proficiency
Machine Learning Implementation
4-5 weeksSkills You'll Build
Scikit-learn for classical ML algorithmsImplementing algorithms you know theoreticallyModel evaluation and validation techniquesFeature engineering in practicePandas for data manipulation
Deep Learning and Neural Networks
4-5 weeksSkills You'll Build
PyTorch fundamentalsNeural network architectures (CNNs, RNNs, Transformers)Training loops and optimizationUnderstanding attention mechanismsGPU computing basics
LLM APIs and Prompt Engineering
3-4 weeksSkills You'll Build
OpenAI and Claude API integrationPrompt engineering for production systemsStructured output handlingToken optimization and cost managementError handling and retry patterns
RAG Systems and Vector Databases
4-5 weeksSkills You'll Build
Embedding models and vector representationsVector database implementation (Pinecone, Chroma)Document chunking strategiesSemantic search and hybrid retrievalRAG evaluation and optimization
AI Application Architecture
3-4 weeksSkills You'll Build
FastAPI for AI backendsAsync programming patternsAPI design best practicesDocker containerizationBasic deployment workflows
Portfolio Projects and Career Launch
4-5 weeksSkills You'll Build
Building portfolio-worthy AI projectsDemonstrating mathematical depth in implementationsGitHub profile optimizationTechnical interview preparationTranslating academic skills to industry language