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Study Notes

Chemistry

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› Course

› GenAI Ops

› 1. Optimizing and Governing AI Systems

· 1. Dashboard Design for AI Cohort Monitoring

· 2. Statistical Drift Detection

· 3. Statistical Drift Detection v2

· 4. Prompt Monitoring — Cohort Metrics

› 2. MTTR Analysis and Operational Resilience

· 1. Technical Architecture Framework

· 2. Cost-Benefit Analysis Methods

· 3. Building Decision Matrices

· 4. Implementation Strategies

· 5. Dialog Review — Fine-Tuning vs RAG

· 6. Assignment

› 3. Create Governance Frameworks

· 1. Why AI Governance Determines Enterprise Success

· 2. Comprehensive AI Governance Framework Components

· 3. Designing Technical Guardrails

· 4. Policy Development and Implementation

· 5. Dialog Review — Stakeholder Dynamics

› 4. Ethical AI Decision-Making and Bias Mitigation

· 1. When AI Bias Becomes Business Risk

· 2. Quantifying Bias and Fairness

· 3. Enterprise AI Risk Management

· 4. Fairness Assessment Tools

› 5. Strategic AI Roadmap Alignment

· 1. Strategic Alignment and Business Value

· 2. Mapping AI Initiatives to Business Objectives

· 3. Systematic Approaches to Assessing Strategic AI Roadmaps

· 4. Using Strategic Alignment Tools to Assess AI Initiatives

· 5. ai-roadmap-analysis-assignment

· 6. AI Roadmap Assignment - Response

· 7. Strategic AI Roadmap Assessment - Summary

› 6. Building AI Centers of Excellence

· 1. From Scattered AI Experiments to Strategic Excellence

· 2. Governance Frameworks for AI Operations at Scale

· 3. Essential Elements of Effective AI Governance Charters

› 7. Analyze Model Complexiity vs Interpretability Trade off

· 1. Why Model Interpretability Can Make or Break Your ML Career

· 2. The Strategic Framework for Complexity-Interpretability Decisions

· 3. Production Trade-off Analysis Framework and Methods

· 4. Hands-on Trade-off Analysis with Production Constraints

› 8. Evaluating Algorithm Performance with Statistical Goal

· 1. Statistical Significance Testing Prevents Million-Dollar Mistakes

· 2. Statistical Testing Foundations for Production ML

· 3. Implementing Statistical Tests for Algorithm Comparison

· 4. Hands-on Statistical Testing Implementation in Python

› 9. Creating Ensemble Models by Combining Mutipe Algorithm

· 1. Netflix Combines 107+ Algorithms Into Billion-Dollar Ensembles

· 2. Ensemble Architecture Fundamentals for Production Systems

· 3. Building Production Ensemble Systems from Scratch

› 10. Feature Importance & Bias Analysis

· 1. Model Interpretability Determines Trust and Fairness

· 2. Understanding SHAP and LIME for Feature Importance

· 3. Detecting and Measuring Bias in ML Models

· 4. Generating SHAP Plots and Interpreting Feature Contributions

› Private Study

› Statistics & ML

· Proportions, SE, CIs, Hypothesis Tests, Cohen's d & Classification Metrics

· Deep-Study Stats Tutor Prompt

· Model Evaluation — Overview

· Logistic Regression — Classification & Decision Boundaries

· Decision Tree – Deep Study Note

· Random Forest – Deep Study Note

· Logistic Regression — Classification & Decision Boundaries

· Logistic Regression & Gradient Descent

· Regularization Techniques

· Decision Boundary

· Stacking — Ensemble Learning

· SVM: Margin, Soft Margin & Kernels

· Boosting Algorithms — Step by Step

· Ensemble Methods — Voting & Stacking

› Study

· Chemistry

· Math

· Physics

› OpenClaw

· CLI Commands

· Docker CLI Commands

· Slash Commands

· Event Processing

· Conversation Q&A v2

· Conversation Q&A v3

· Conversation Summary

› AI Next Era

· Modern AI Agents — From Concepts to Working Systems

Chemistry

Notes coming soon.