🤖 AI & Machine Learning
AI For Everyone
AI For Everyone is a non-technical, business-friendly introduction to artificial intelligence that empowers learners acr...
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Sections
1. Orientation and Course Overview0 views
Get oriented to the course goals, structure, resources, and how to succeed.
15 topics (15 versions)
1.1Why this course
1.2Learning goals and outcomes
1.3Who this course is for
1.4Syllabus and weekly flow
1.5What’s included and time estimates
1.6Navigating videos and readings
1.7Using the forum and community
1.8Lecture notes and references
1.9Quizzes and assignments overview
1.10Intake survey and feedback
1.11Optional content guidance
1.12Tips for staying on track
1.13How to take notes effectively
1.14Terminology expectations
1.15Support and help channels
2. AI Fundamentals for Everyone0 views
Build a clear, intuitive understanding of what AI is and where it adds value.
15 topics (15 versions)
2.1What is AI
2.2Narrow vs general AI
2.3Why AI matters now
2.4AI vs rules-based software
2.5Patterns, predictions, and decisions
2.6Human-in-the-loop concept
2.7Uncertainty and confidence
2.8Data to value pipeline
2.9The AI lifecycle at a glance
2.10Where AI shows up in products
2.11Framing problems for AI
2.12When AI is not needed
2.13Ethical mindset from day one
2.14Common myths and realities
2.15A simple end-to-end example
3. Machine Learning Essentials0 views
Grasp the core ideas of machine learning without math or code.
15 topics (15 versions)
3.1Supervised learning
3.2Unsupervised learning
3.3Reinforcement learning
3.4Features and labels
3.5Training vs inference
3.6Loss and optimization
3.7Model evaluation basics
3.8Overfitting and underfitting
3.9Bias–variance tradeoff
3.10Cross-validation basics
3.11Choosing metrics
3.12Data leakage pitfalls
3.13Deployment considerations
3.14Online vs batch inference
3.15Common algorithm families
4. Understanding Data0 views
Learn the data concepts that underpin effective AI systems.
15 topics (15 versions)
4.1Data types and modalities
4.2Structured vs unstructured data
4.3Data sources and collection
4.4Data quality dimensions
4.5Sampling strategies
4.6Data labeling basics
4.7Annotation tools overview
4.8Train, dev, and test splits
4.9Data pipelines and ETL
4.10Feature engineering basics
4.11Privacy and consent basics
4.12Data governance fundamentals
4.13Dataset documentation practices
4.14Synthetic and augmented data
4.15Data drift and monitoring
5. AI Terminology and Mental Models0 views
Build a shared vocabulary and simple mental models for AI discussions.
15 topics (15 versions)
5.1AI, ML, and DL definitions
5.2Models and algorithms
5.3Parameters and hyperparameters
5.4Loss functions and metrics
5.5Training, validation, and testing
5.6Precision, recall, and F1
5.7ROC curves and AUC
5.8Confusion matrix basics
5.9Embeddings and representations
5.10Transfer learning overview
5.11Fine-tuning concepts
5.12Generative vs discriminative
5.13Prompting fundamentals
5.14Retrieval-augmented generation
5.15Interpretability at a glance
6. What Makes an AI-Driven Organization0 views
Understand the strategies, culture, and systems behind successful AI companies.
15 topics (15 versions)
6.1Data strategy foundations
6.2Leadership alignment
6.3Use case portfolio design
6.4Talent and roles mix
6.5Culture of experimentation
6.6MLOps at a glance
6.7Infrastructure and platforms
6.8Build vs buy decisions
6.9Vendor and tool evaluation
6.10Risk and compliance posture
6.11Responsible AI governance
6.12KPIs and value tracking
6.13Budgeting and funding models
6.14Change management essentials
6.15Scaling beyond pilots
7. Capabilities and Limits of Machine Learning0 views
Develop realistic expectations of what ML can and cannot do.
15 topics (15 versions)
7.1What ML can do well
7.2What ML cannot do yet
7.3When to prefer rules
7.4Data volume requirements
7.5Label quality requirements
7.6Generalization limitations
7.7Robustness and edge cases
7.8Causation vs correlation
7.9Interpretability limitations
7.10Safety and reliability bounds
7.11Latency and compute tradeoffs
7.12Maintenance and model decay
7.13Cost and ROI considerations
7.14Human oversight boundaries
7.15When not to automate
8. Non-Technical Deep Learning0 views
Demystify deep learning concepts with plain-language intuition.
15 topics (15 versions)
8.1Neural networks intuition
8.2Layers, neurons, and activations
8.3Representation learning idea
8.4Convolutional networks overview
8.5Sequence models overview
8.6Attention mechanisms idea
8.7Transformers in plain language
8.8Foundation models overview
8.9Transfer and fine-tuning paths
8.10Prompting and chaining basics
8.11RAG and grounding concepts
8.12Multimodal models overview
8.13Scaling laws intuition
8.14Strengths and weaknesses
8.15Everyday DL use cases
9. Workflows for ML and Data Science0 views
Learn practical, repeatable workflows that drive successful AI projects.
15 topics (15 versions)
9.1ML project lifecycle
9.2Problem framing steps
9.3Data collection planning
9.4Labeling and QA process
9.5Baseline model first
9.6Error analysis loops
9.7Iteration and ablation
9.8Experiment tracking habits
9.9Model selection criteria
9.10Evaluation plan design
9.11Deployment plan outline
9.12Monitoring and alerts
9.13Feedback and retraining
9.14Data science workflow map
9.15Collaboration checkpoints
10. Choosing and Scoping AI Projects0 views
Select high-impact, feasible AI projects and define success clearly.
15 topics (15 versions)
10.1Aligning to business goals
10.2Opportunity discovery methods
10.3Feasibility assessments
10.4Data availability audits
10.5Risk and constraint analysis
10.6Estimating impact and ROI
10.7Quick wins vs moonshots
10.8Pilot scope and resources
10.9Defining success metrics
10.10Stakeholder and user mapping
10.11Compliance and ethics review
10.12Build vs buy tradeoffs
10.13Vendor pilot evaluation
10.14Prioritization frameworks
10.15Roadmap and next steps
11. Working with AI Teams and Tools0 views
Coordinate roles, communication, and toolchains for effective delivery.
15 topics (15 versions)
11.1Core roles on AI teams
11.2PM responsibilities in AI
11.3Data scientist vs engineer
11.4Machine learning engineer role
11.5Cross-functional partners
11.6Communication cadences
11.7Documentation best practices
11.8Toolchain overview
11.9Cloud platforms and services
11.10AutoML and no-code options
11.11LLM tooling landscape
11.12Data labeling vendors
11.13Security and access control
11.14Collaboration etiquette
11.15Remote and hybrid workflows
12. Case Studies: Smart Speaker and Self-Driving Car0 views
Apply concepts to real-world systems to see tradeoffs and decisions in action.
15 topics (15 versions)
12.1Smart speaker problem framing
12.2Wake word detection basics
12.3Speech recognition pipeline
12.4Natural language understanding
12.5Personalization and context
12.6Privacy and consent tradeoffs
12.7Edge vs cloud decisions
12.8Error analysis in practice
12.9Voice assistant metrics
12.10Self-driving stack overview
12.11Perception systems basics
12.12Prediction and forecasting
12.13Motion planning basics
12.14Safety cases and testing
12.15Regulation and public trust
13. AI Transformation Playbook0 views
Follow a structured approach to scale AI across an organization.
15 topics (15 versions)
13.1Vision and strategy setting
13.2Executive sponsorship
13.3Capability and gap assessment
13.4Data platform foundations
13.5Use case pipeline management
13.6Governance and guardrails
13.7Talent acquisition and upskilling
13.8Partner and vendor ecosystem
13.9Operating model choices
13.10Funding and portfolio management
13.11Change management tactics
13.12Measurement and OKRs
13.13Scaling from pilot to production
13.14Communicating wins and learnings
13.15Sustaining momentum
14. Pitfalls, Risks, and Responsible AI0 views
Identify and mitigate ethical, technical, and operational risks.
15 topics (15 versions)
14.1Sources of bias
14.2Fairness concepts
14.3Bias mitigation approaches
14.4Adversarial attack basics
14.5Robustness testing methods
14.6Privacy risks and harms
14.7Misuse and adverse applications
14.8Responsible AI frameworks
14.9Transparency and explainability
14.10Human oversight practices
14.11Red teaming and stress tests
14.12Incident response planning
14.13Model and data documentation
14.14Legal and regulatory context
14.15Ethics review checklists
15. AI and Society, Careers, and Next Steps0 views
Explore societal impacts and craft your personal plan to apply AI.
15 topics (15 versions)
15.1AI and developing economies
15.2Industry and sector impacts
15.3Jobs displaced and created
15.4Augmentation over automation
15.5Reskilling and upskilling paths
15.6Inclusion and accessibility
15.7Policy and public discourse
15.8Environmental considerations
15.9Personal learning roadmap
15.10First AI project checklist
15.11Communicating with stakeholders
15.12Portfolio and case study ideas
15.13Preparing for the final quiz
15.14Engaging with the community
15.15Course wrap-up and call to action