Comprehensive, hands-on training designed to prepare you for high-impact AI roles.
Become AI-ready in 4 weeks with strong ML foundations, a portfolio Face Recognition project, and the skills to pursue high-demand AI engineering roles.
Build interview-ready skills with hands-on projects including a team-based Face Recognition Capstone that showcases your end-to-end AI deployment abilities.
Get personalized guidance through 4 live interactive sessions weekly to discuss curriculum topics, projects, and technical questions with your Carnegie Mellon-trained instructor.
Position yourself for high-paying AI roles with a portfolio-ready Capstone Project and industry-recognized skills that demonstrate your practical deployment abilities.
Traditional AI education takes years. We condense it into 4 intensive weeks without sacrificing depth or quality.
Fast-track to AI careers: Traditional AI education takes years. We condense it into 4 intensive weeks without sacrificing depth or quality.
Accelerated Learning Path: Skip the academic fluff. Go straight from Linear Algebra basics to building production-ready AI models with TensorFlow and PyTorch.
Hands-On From Day One: Forget passive lectures. You’ll code actual ML models, train neural networks, and deploy a Face Recognition System — not just watch someone else do it.
You’ll build a strong foundation in math and Python AI tools, complete a Data Exploration mini-project, then advance into Machine Learning, Deep Learning, and key AI domains like NLP, Computer Vision, and Speech Recognition.
In week one, you’ll establish the math and Python foundations essential for AI, from Linear Algebra to practical data analysis.
Learn Linear Algebra and Calculus fundamentals for Machine Learning, including vectors, matrices, gradients, and optimization basics.
Understand how to handle and explore real-world datasets using Pandas for data manipulation and Matplotlib for visualization.
Become comfortable using NumPy for array operations and mathematical computations essential for AI algorithms.
Join our 4-week AI Engineering intensive designed specifically for software developers. Build practical, portfolio-ready skills from Linear Algebra foundations to deploying advanced AI systems.
Follow our proven week-by-week curriculum with 80+ hours of blended learning. Attend 4 mandatory live sessions weekly while completing self-paced modules and hands-on projects between sessions.
Connect directly with your Carnegie Mellon-trained AI instructor during live sessions for lectures, Q&A, and coding guidance. Work collaboratively on projects with your cohort throughout the program
The next cohort starts
January 16, 2026
Deadline for applications
December 15, 2025
Application deadline in
29 days
Miracle James is an AI Engineer and Researcher currently completing her Post Graduate studies in Engineering Artificial Intelligence at Carnegie Mellon University, with a focus on machine learning, computer vision, and large language models. She has co-authored research exploring leukemia detection through medical imaging and methods to spot deep-fakes in facial recognition systems, positioning her as an ideal mentor for entry-level software engineers seeking to master the rigor of AI engineering. She excels at translating complex concepts into actionable engineering skills, ensuring students grasp everything from the calculus driving neural networks to the intricacies of TensorFlow and PyTorch.
For participants in this cohort, Miracle offers a definitive competitive edge as her expertise is directly aligned with the course’s final objective. As you engineer a Face Classification and Verification System for your capstone project, you will leverage insights from her published work on TSANet, accepted at the prestigious International Conference on Computer Vision (ICCV 2025). Her research specifically tackles face anti-spoofing and biometric security, meaning you will not just learn to build facial recognition models but will learn to architect them for real-world robustness under the guidance of a subject matter expert defining the standards of the field.
Miracle is the ideal instructor to guide you from code to intelligence because she combines academic excellence with diverse real-world applications. As a former Teaching Assistant for Machine Learning System Design, she is perfectly equipped to guide you through the course’s technical stack, spanning Python, Git, and deep learning frameworks. Her portfolio, which includes Automated Leukemia Detection from Medical Images and Embedded Weather Monitoring Systems, demonstrates her commitment to the end-to-end project lifecycle. This breadth of experience ensures that under her mentorship, you will not only write code but also develop the strategic mindset of a high-impact AI Engineer ready for the modern tech landscape.
Intermediate programming skills, preferably in Python. No prior AI/ML experience is necessary, as we start from the foundational math.
The course is an intensive 4-week cohort-based program.
Python, NumPy, Pandas, scikit-learn, TensorFlow/PyTorch, Jupyter Notebooks, and concepts like CNNs, LLMs, and transfer learning.
Yes, you complete three mini-projects and a final, collaborative Face Recognition Capstone Projectfor your portfolio.
Yes, you have four live instructor-led sessions each week (lectures, Q&A, and group discussions) for active support and mentorship.
You will be able to design, build, train, and deploy AI/ML models, preparing you for entry-level to intermediate AI Engineering roles with confidence.
