CPMAI Concepts In-Depth Video Study Guide

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Course curriculum

1. Introduction
2. Breaking the Digital Transformation Logjam
3. The Dream of the Intelligent Machine
4. AI in Action
5. Human Intelligence: The Essential Blueprint for AI
6. AI & The Brain
7. Demystifying AI
8. Solving the Core Problem
9. AI Reality Check
10. The Turing Test
11. Cognitive Technology
12. Navigating Myths for Project Success
13. Augmented Intelligence
14. AI Origins
15. Navigating the AI Hype Cycle
16. Symbolic AI
17. The Modern AI Summer
18. AI: The Engine of the “Always-On” Era
1. Introduction
2. Evolution of Goal-Driven Systems
3. The Deterministic vs. Probabilistic Framework for AI Suitability
4. Seven Patterns of AI: The Recognition Pattern
5. Seven Patterns of AI: The Conversational Pattern
6. Seven Patterns of AI: Predictive Analytics & Decision Support
7. Seven Patterns of AI: Patterns & Anomalies
8. Seven Patterns of AI: The Goal-Driven Systems Pattern
9. Seven Patterns of AI: The Hyperpersonalization Pattern
10. Seven Patterns of AI: The Autonomous Systems Pattern
11. Mastering Hybrid AI: Combining Patterns for Real-World Success
1. Introduction
2. Why AI Projects Fail
3. AI vs. Traditional Software Projects
4. The AI ROI Reality Check
5. Data Integrity
6. Escaping AI POC Purgatory
7. Beyond the Lab: Bridging the AI Performance Gap
8. AI is Never Done: Mastering the Continuous Model Lifecycle
9. Avoiding the AI Vendor Hype Trap
10. Breaking the Overpromise Cycle
11. Navigating the Uncanny Valley
12. Waterfall Methodology
13. Why Iterative Beats Waterfall
14. Mastering Lean Methodology
15. The 12 Principles of Agile
16. Data vs Code: Bridging the Methodology Gap
17. Why Standard Agile Fails Data Projects
18. Agile for Data
19. Agile Data Evolution
20. Agile + Data: Bridging the Gap with CPMAI
21. Mastering CRISP-DM
22. CPMAI: The Agile Framework for AI Success
23. CPMAI: The 6-Phase Lifecycle for AI Project Success
24. Mastering the CPMAI Workbook
1. Introduction
2. The CPMAI Business Case
3. AI vs Automation
4. The DIKUW Pyramid
5. The AI Sweet Spot
6. AI Success by Design
7. AI Project Scoping
8. The Architecture of AI Project Scoping
9. The AI ROI Roadmap
10. The AI Shortcut: Accelerating Time-to-Value with Foundation Models
11. Why AI Pilots Beat Proof of Concepts (PoC)
12. The AI Project Team
13. Data Science Demystified
14. Citizen Data Scientist
15. The Triple-Layer Framework for AI Success
16. The Foundation of Trust
17. AI Project Readiness
18. CPMAI Phase I: The AI Go/No-Go Decision
19. Smart CI: Accelerating Software Development with AI-Driven Predictive Testing
20. Predictive Maintenance: The NASA Strategy for Optimal Uptime
21. Safeguarding the Brand: AI-Powered Content Moderation
1. Introduction
2. Machine Learning: The Engine of Adaptive Intelligence
3. The Architecture of Machine Learning: From Process to Artifact
4. AI Foundations
5. Brains vs Brawn: Navigating the Search Space
6. The Recipe vs The Cake
7. The Machine Learning Lifecycle
8. The AI Methodology Roadmap
9. Mastering Machine Learning Classification
10. The Geometry of AI: Demystifying Supervised Learning
11. Mastering Linear Regression: Predicting the “How Much”
12. Unlocking Hidden Patterns: The Essentials of Cluster Analysis
13. Mastering the Curse: Finding the “Sweet Spot” of Data Features
14. From Raw Data to Powerful Predictions: A Guide to Feature Engineering
1. Introduction
2. Mastering Machine Learning Classification
3. Mastering Machine Learning: The Regression Toolbox
4. Naive Bayes: The Fast & “Naive” Approach to Classification
5. SVM & The Kernel Trick: Mastering the “Widest Road”
6. Decoding the Decision Tree: The “White Box” of Machine Learning
7. Strength in Numbers: The Power of Ensemble Modeling
8. Random Forests vs Boosted Trees: Mastering Ensemble Learning
9. A Guide to Clustering Algorithms
10. K-Means vs Gaussian Mixture Model (GMM): Hard vs Soft Clustering
11. PCA vs t-SNE: Mastering Dimensionality Reduction
12. Reinforcement Learning: A Comprehensive Strategic Study Guide
1. Introduction
2. Neural Networks: Engineering Artifical Intelligence
3. Demystifying Artificial Neural Networks: From Neurons to Predictions
4. Beyond the Straight Line: Why Neural Networks Master Complexity
5. Neural Networks: The Universal Machine Learning Architecture
6. Inside the Artificial Neuron: The Mechanics of Machine Learning
7. The Perceptron: The Foundation of Artificial Intelligence
8. Architectures of Depth: Hidden Layers Drive Deep Learning
9. Feed-Forward Neural Networks: The Vanilla Blueprint
10. The Mechanics of Machine Learning Optimization: How Models Learn
11. Inside the Brain of AI: The Mechanics of Neural Network Training
12. Why GPUs Are Great for AI
13. Mastering the Training Loop: Epochs, Batches & Learning Curves
14. Navigating the Deep Learning Zoo: Matching Models to Tasks
15. Sequential Intelligence: From RNN Loops to LSTM Gates
16. Decoding Convolutional Neural Network (CNNs)
17. Neural Architectures: Understanding Autoencoders and GANs
18. Decoding Boltzmann Machines: From Neural Networks to Recommendation Engines
1. Introduction
2. Generative AI: From Analyzing Data to Creating the Future
3. Navigating Generative AI: Strategic Growth vs Critical Risk
4. Generative AI: Navigating Inherent Risks and Ethical Failures
5. Transformer Revolution: Inside the Engine of Modern AI
6. LLMs Essentials: Foundations, Power, and Pitfalls
7. Inside the Machine: How Large Language Models Actually Work
8. The Foundation Model Revolution: From General Engines to Specialized Experts
9. Self-Supervised Learning: The Engine of Foundation Models
10. OpenAI: The Mission-Driven Blueprint for AGI
11. GPT & ChatGPT: From Raw Engine to the Drive’s Seat
12. The Architect’s Guide to Strategic Prompt Engineering
13. LLM Fine-Tuning: From Generalists to Specialized Experts
14. Vector Databases: Unlocking Long-Term Memory for AI
15. LangChain: The Orchestration Engine for LLM Development
16. Sculpting from Static: How Diffusion Models Create AI Art
17. The Big Three: A Guide to DALL-E, Stable Diffusion, & Midjourney
18. Mastering the AI Image Toolkit: Beyond Text-to-Image
19. Augmented Intelligence: Your Human-Centric Digital Co-Pilot
1. Introduction
2. Data-First: The Fuel of Modern AI
3. Training Data: The Essential for AI
4. Mastering the Seven V’s: A Diagnostic Framework for Big Data & AI
5. Mastering the Scale: A Guide to Big Data Volume
6. Mastering the Big Data Variety Spectrum: From Raw Volume to Actionable AI
7. The Veracity Imperative: Engineering Trust in Big Data
8. Velocity: Mastering the Power of Data in Motion
9. Scalable Blueprints: The Big Data Foundation for AI Success
10. MapReduce & Hadoop: The Architecture of Big Data Efficiency
11. The Data Analytics Journey: From Hidsight to Foresight
12. Data Science: Bridging the Gap to Predictive Intelligence
13. The DIKUW Pyramid: Transforming Raw Data into Strategic Wisdom
14. The Foundation of AI Success: A Big Data Strategy
15. The Data Lifecycle: From Birth to Destruction
16. Data Stewardship: Turning Data Policy into Action
17. The Data Custodian: Bridging the Gap Between Policy and Practice
18. Data Quality Management; Building the Foundation for Trusted Insights
19. Data Security: Building a Multi-Layered Defense-in-Depth
20. Data Governance: The Constitution for Information Assets
21. Mastering the Data Asset: A Guide to Management & Planning
1. Introduction
2. CPMAI Phase II: Mastering Data Understanding
3. Data Collection: Fueling the AI Engine
4. Training Data: The “Textbook’ for AI
5. Ground Truth: The “Answer Key” for ML
6. Synthetic Data: Bridging the Gap in AI Development
7. AI Data Appetite: Right-Sizing Your Project Requirements
8. Start Small, Think Big: The Data-Efficient AI Blueprint
9. Bridging the Data Gap: A Guide to Synthetic Data Engineering
10. The AI Development Shortcut: Pre-trained Models & Transfer Learning
11. The CPMAI Phase II Go/No Go Milestone: Guarding Your AI Project
12. The CPMAI Pivot: Navigating Phase II to Phase I Iteration
1. Introduction
2. Data Debt: The Compounding Cost of Mismanaged Information
3. Data Engineering: The 80% Reality of AI Success
4. Master the AI Lifecycle: Architecting Training & Inference Pipelines
5. Model-Ready: The Data Preparation Blueprint for Machine Learning
6. AI-Specific Data Preparation: Beyond Basic Cleaning
7. Data Refinement: The Path to High-Performance AI
8. Mastering Data Augmentation: Boosting AI Robustness
9. From Raw Data to AI Perception: The Foundations of Annotation & Sensor Fusion
10. Data Labeling: The Achilles Heel of Supervised AI
11. Data Selection & Sampling: Optimizing the Machine Learning Pipeline
12. The Path to Generalization: Validation vs Test Data
1. Introduction
2. CPMAI Phase III: From Messy Data to Model-Ready Fuel
3. Data Preparation Pipelines: The Blueprint for Reliable AI
4. Safeguarding the AI Lifecycle: Data Privacy & Security
5. Gen AI Data Prep: From Manual Bottleneck to Intelligent Automation
6. Data Labeling: Building the “Ground Truth” for AI
7. Gatekeeping the Pipeline: Phase III Go/No Go Decision
8. CPMAI Phase III: The Reality Check (Navigating Iterative Loops)
1. Introducution
2. Mastering ML Model Training: Balancing Complexity & Resources
3. The Science of Selection: Choosing the Right Machine Learning Algorithm
4. The Machine Learning Algorithm Selection Roadmap
5. Fast-Track AI: A Guide to Accelerated Model Training
6. The ML Platform Myth: Why You Need a Stack, not a Monolith
7. The AI Language Decision Matrix: Choosing Your Development Stack
8. Navigating the ML EcoSystem: Tools, Frameworks, & Exam Essentials
9. Data Science Notebooks: Unifying the Analytical Workflow
1. Introduction
2. AutoML: Democratizing Data Science
3. MLaaS: The ‘Buy’ Strategy for Modern AI Integration
4. CPMAI Phase IV: The Strategic Roadmap to AI Model Development
5. The CPMAI Phase IV Go/No Go Gate: Deciding the Future of Your AI Model
6. The AI Iteration Map: Diagnostic Strategies for Model Failure
1. Introduction
2. Mastering Model Validation: The Path to ML Generalization
3. The Machine Learning ‘EKG’: Diagnosing Model Health
4. The Bias-Variance Balance: Mastering Model Generalization
5. Mastering Model Tuning: The Art of Hyperparameter Optimization
6. Mastering the Fit: Balancing Bias and Variance for Better Models
7. Balancing the Scales: The Bias-Variance Tradeoff in ML
8. Mastering Cross-Validation: The Key to Robust ML Models
9. Decoding the Confusion Matrix: Beyond Simple Accuracy
10. Mastering the ROC Curve: Balancing Sensitivity & Precision
11. Beyond Accuracy: Measuring the Real-World Value of AI
12. AI Technology KPIs: Bridging the Gap Between Accuracy and Reality
1. Introduction
2. Is Your Model Ready for Production? The CPMAI Evaluation Framework
3. Strategic Model Evaluation: Beyond Technical Accuracy
4. Model Iteration: The MLOps Lifecycle for AI Success
5. AI Survival Guide: Navigating Data Drift & Model Decay
6. Timing is Everything: When to Retrain a Model
7. The AI Assembly Line: Mastering Automated Model Retraining
8. CPMAI Phase V: The AI Model Deployment Gatekeeper
9. CPMAI Iteration: The Diagnostic Guide to Phase - Gate Failures
1. Introduction
2. From Lab to Life: Mastering the AI Inference Phase
3. Bridge to Inference: Mastering AI Operationalization
4. AI Operationalization: Choosing Your Prediction Architecture
5. Scaling AI: The Microservice Model Deployment Architecture
6. AI Deployment Decoder: Choosing the Right Home for Your Model
7. Edge AI: Intelligence on the Brink
8. AI Under Your Roof: A Guide to Operationalizing ML On-Premise
9. Mastering the AI Cloud: From Infrastructure to MLaaS
10. ML in the Cloud: Strategic Deployment Playbook
11. Model Lifecycle Management: The Perpetual Loop of AI Success
12. From DevOps to MLOps Mastery: Bridging the Divide
13. Decoding MLOPs: The Dual Foundations of AI Strategy
14. Mastering the AI Data Lifecycle: A Guide for Robust MLOps
1. Introduction
2. From Experiment to Asset: Mastering Model Operationalization
3. The Four Essential Environments of AI Success
4. Inside the AI Lab: Mastering the Model Development & Training Environment
5. The Data Kitchen: Why Data Engineering is the Foundation of AI
6. Model Scaffolding: Turning Mathematical Models into Usable Apps
7. From Lab to Live: Mastering the AI Operational Environment
8. Governing Generative AI: From Scaffolding to Production
9. Model Governance: The Rulebook for Secure & Accountable AI
10. The AI Governance Blueprint: Why “Set it & Forget it” Fails
11. AI Model Monitoring: The ‘Check Engine Light’ for Your Deployed Models
12. Model Security: Defending the AI Frontier
13. CPMAI Phase VI: The Gateway to Iterative AI Success
14. The CPMAI Iterative-Feedback Loop: Solving AI Failures at the Root
1. Introduction
2. The AI Disruption Blueprint: Navigating Risks to Build Trust
3. Beyond the Hype: Tackling AI Fears to Build Trust
4. Beyond the Hype: Tackling the Real Concerns of AI
5. Mastering the Trustworthy AI Spectrum: From Ethics to Explanation
6. Ethical AI: The Moral Blueprint for Trustworthy Systems
7. AI & The Evolution of Work: From Job Killers to Category Evolution
8. The Amazon Paradox: How Automation Fuels Human Growth
9. Decoding Algorthmic Discrimination: From Biased Data to “Weapons of Math Destruction”
10. AGI vs Narrow AI: Reality Check for Ai Professionals
11. Navigating the Singularity: From Futuristic Theory to Project Management Reality
12. Navigating the Uncanny Valley: Why “Almost Human” Falls
13. Responsible AI: From Ethical Theory to Practical Action
14. AI Privacy: Why Data Protection is the Foundation
15. GDPR Essentials: The Global Standard for Data Privacy
16. PII vs PHIZ: A Guide to Protecting Identity in AI
17. The Blueprint for Trustworthy AI: Safety, Reliability & Security
18. The Weaponization of AI: Understanding Malicious Intent, Tactics, and Tools
19. Pseudo AI: The High Cost of Deceptive Automation
20. AI System Transparency: Beyond the Algorithm
21. Beyond the Black Box: A Guide to AI Visibility and Trust
22. AI Disclosure: The Project Manager’s Guide to Building Trust
23. AI System Consent: Navigating Ethical Automation
24. Mastering Informational Bias Measurement: A Guide for Fair AI
25. AI Governance: Transitioning from Principles to Practice
26. AI Audit Trails: The Blueprint for Accountability
27. Human Recourse: The Right to Contest AI Decisions
28. Keeping an Eye on AI: A Framework for Continuous Monitoring
29. Decoding the Black Box: Making AI Understandable
30. Illuminating the Black Box: A Guide to Explainable AI (XAI)
31. The AI Explainability Spectrum: Balancing Performance & Trust
32. Interpretable AI: Navigating the Map of Machine Logic
33. AI Privacy: The Legal Blueprint for Project Managers
34. Balancing Progress & Privacy: The Global Governance of Facial Recognition
35. Mastering Algorithmic Regulation: From Ethics to Mandatory Compliance
36. From Ethics to Law: The Global Shift in AI Regulation
37. Human-Centric AI: A Framework for Ethical & Trustworthy Systems
38. The Ethical & Responsible AI Playbook: A Five-Pillar Framework
39. Narrow AI Reality Check: Navigating the Limits of Modern Technology

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293 lessons
31 hours of video content

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CPMAI Mastery System

CPMAI Concepts In-Depth Video Study Guide

Course curriculum

What is Artificial Intelligence

Applications of AI

Best Practices & Methodologies for Successful AI Implementation

CPMAI Phase I - Business Understanding

Machine Learning Fundamental Concepts

Machine Learning Algorithms - Part 1

Machine Learning Algorithms - Part 2

Generative AI, Transformer Models & LLMs

Managing Data for AI

CPMAI Phase II - Data Understanding

Data Preparation for AI

CPMAI Phase III - Data Preparation

Machine Learning Development Tools & Platforms

CPMAI Phase IV - Model Development.

Model Evaluation and Testing

CPMAI Phase V Model Evaluation

Model Operationalization

CPMAI Phase VI - Model Operationalization

Trustworthy AI Concepts

About this course

Discover your potential, starting today