Between Jun 2026 – Sep 2026 I ran a deliberate learning sprint alongside my TPM work at Google — 11 Coursera credentials and 69 courses in program leadership, AI agents, and machine learning. Each one links to its coursera.org verification page and the official PDF.
Completed Jun 2026 – Sep 2026 · Issued through Coursera by Duke University, Microsoft, IBM, SkillUp, Vanderbilt University, DeepLearning.AI, Stanford Online, Google · Every credential is independently verifiable.
Running programs and projects end to end — predictive, agile and hybrid.
Coordinating project managers and teams through all phases of a program, choosing predictive, agile, or hybrid ways of working, and tying program outcomes to strategic organizational goals.
Initiating, planning, and running both traditional and agile projects, with hands-on, practice-based assessments and a real-world capstone.
Shipping ML products and using generative AI to run programs.
How machine learning works, when and how to apply it, the data science process and best practices for guiding ML projects, and the ethical and legal considerations of shipping AI products.
Using generative AI to enhance program execution, decision-making, and stakeholder engagement: analyzing project data, automating reporting, optimizing resources, and assessing risk across the program lifecycle.
RAG, vector databases, multi-agent architectures and tool-using agents.
Building advanced generative AI applications with RAG, vector databases, and agent frameworks; designing multi-agent architectures with LangGraph and CrewAI; building tool-using agents over MCP.
Designing, building, and deploying tool-using AI agents in Python: agent architectures, memory systems, custom GPTs, advanced prompt engineering, and trustworthy AI practices.
Generative AI across research, communication, analysis and app building.
Applying AI across the work where it is transforming roles: brainstorming, research, communication, content creation, data analysis, and coding, with a portfolio of 20+ AI-built artifacts and a custom AI solution.
From regression and neural networks to statistics and data storytelling.
Andrew Ng's foundational program: linear and logistic regression, neural networks, decision trees, clustering, anomaly detection, recommender systems, and reinforcement learning, with best practices for building ML models.
Exploring large datasets, applying statistical and regression analysis, and building predictive models to extract insights, with a capstone project and preparation for advanced analytics roles.
Validating and cleaning data, detecting bias and applying ethical practices, analyzing with spreadsheets, and using generative AI to accelerate insights and presentations for stakeholders.
Python, Git, Linux and cloud automation for real-world IT tasks.
Writing Python to automate real-world IT and systems tasks, using Git and GitHub, troubleshooting and debugging complex problems, and applying automation at scale with configuration management and the cloud.