AI Development Mastery Program
Learn from industry veterans who've built real AI systems for Fortune 500 companies. Our hands-on curriculum combines theoretical foundations with practical implementation skills.
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Kai Thornfield
Former Tesla AI team member with 8 years in production ML systems. Specializes in computer vision and neural network optimization.
Dr. Senna Blackwood
PhD in Computational Linguistics, published 40+ papers. Previously at Google Research working on transformer architectures.
Zara Middleton
Built AI products at Microsoft and Uber. Expert in translating business requirements into technical implementations.
Expert-Guided Learning Journey
Our Teaching Philosophy
We believe the best way to master AI is through real project work alongside experienced practitioners. Each instructor brings current industry experience and proven track records.
- Small cohorts of 12 students maximum for personalized attention
- Weekly one-on-one mentoring sessions with industry experts
- Live code reviews and pair programming opportunities
- Direct access to instructor networks for career guidance
Real-World Case Studies
Learn from actual projects our instructors have led at major tech companies
Autonomous Vehicle Vision System
Kai led development of real-time object detection system processing 30 fps video streams. The system needed 99.9% accuracy for safety-critical applications while running on embedded hardware.
What You'll Learn
- Optimizing neural networks for edge computing constraints
- Handling class imbalance in safety-critical datasets
- Real-time inference architecture patterns
- Testing strategies for mission-critical AI systems
Multi-Language Search Engine
Dr. Blackwood architected semantic search system handling 50+ languages with contextual understanding. The project required novel approaches to cross-lingual embeddings and cultural nuance detection.
Key Insights
- Cross-lingual transfer learning techniques
- Handling cultural context in embeddings
- Scaling transformer models for production
- Evaluation metrics for multilingual systems
12-Week Curriculum
Progressive learning path designed from real industry experience. Each phase builds practical skills you'll use in professional AI development.
Foundations & Mathematics
Build solid mathematical foundation and understand core concepts. We focus on intuitive understanding before diving into complex implementations.
Linear Algebra
Vector operations, matrix transformations, eigenvalues in ML context
Statistics
Probability distributions, Bayesian thinking, hypothesis testing
Python Fundamentals
NumPy, Pandas, Matplotlib for data manipulation and visualization
Machine Learning Core
Master traditional ML algorithms and understand when to use each approach. Heavy emphasis on practical implementation and real dataset work.
Supervised Learning
Regression, classification, ensemble methods with scikit-learn
Feature Engineering
Data preprocessing, selection, dimensionality reduction techniques
Model Evaluation
Cross-validation, metrics, avoiding overfitting in practice
Deep Learning & Neural Networks
Dive into neural architectures and learn to build systems that work at scale. Focus on practical applications rather than just theory.
Neural Architecture
CNNs, RNNs, attention mechanisms with PyTorch implementation
Computer Vision
Image classification, object detection, transfer learning
NLP Applications
Text processing, sentiment analysis, transformer fine-tuning
Production & Deployment
Learn to deploy and maintain AI systems in production environments. Cover MLOps, monitoring, and scaling challenges from instructor experience.
Model Deployment
Docker, cloud platforms, API development, serving optimization
Monitoring & Maintenance
Data drift detection, model retraining, A/B testing frameworks
Portfolio Project
End-to-end system showcasing your skills for job applications
Join Our Next Cohort
We're accepting applications for our fall 2025 and spring 2026 cohorts. Each program is limited to 12 students to ensure personalized attention and meaningful mentorship.
Upcoming Program Dates
Application deadlines are typically 6 weeks before program start dates