Complete course catalog with detailed descriptions. Courses marked with * are included in the curated Track 1 (Leadership) or Track 2 (Engineering) roadmaps.
Legend:
- ★ Track 1 = Professional/Leadership Roadmap course
- ★ Track 2 = Technical/Engineering Roadmap course
- ★ Both Tracks = Required in both roadmaps
TRACK COURSES (Core Roadmap Requirements)
These courses form the structured learning sequences in Track 1 and Track 2. Complete these in order for maximum credential impact.
★ Elements of AI *
Track: Track 1 (Phase 1) Provider: University of Helsinki / MinnaLearn
URL: https://www.elementsofai.com/ Duration: ~30 hours (self-paced) Cost: Free
Description: Created by the University of Helsinki and MinnaLearn, this course demystifies AI for non-technical audiences. Covers what AI is (and is not), machine learning basics, neural networks, and societal implications. No programming required. Over 900,000 learners from 170+ countries. Certificate available. Designed to help 1% of European citizens understand AI fundamentals.
Why Track 1: Establishes foundational AI literacy with prestigious European university credential. Perfect starting point for leaders who need conceptual understanding before strategic application.
★ IBM SkillsBuild AI Fundamentals *
Track: Track 1 (Phase 1) Provider: IBM SkillsBuild
Duration: ~10-15 hours across multiple badges Cost: Free
Description: IBM’s free learning platform offering AI fundamentals, machine learning basics, and responsible AI practices. Earn digital badges verified through Credly and displayable on LinkedIn. Topics include AI concepts, chatbots, computer vision, and enterprise AI applications. Hands-on projects using IBM Watson tools.
Why Track 1: Quick wins with recognized enterprise brand. Multiple badges demonstrate progressive learning. IBM credential carries weight in enterprise environments.
★ Microsoft AI Fundamentals (AI-900) *
Track: Track 1 (Phase 1) Provider: Microsoft Learn
URL: https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/
Duration: ~40-60 hours preparation Cost: $99 exam fee
Description: Entry-level certification validating understanding of machine learning concepts, computer vision, natural language processing, and conversational AI on Azure. Approximately 40-60 questions over 60 minutes. No expiration; renew through free online assessment annually. Free learning path on Microsoft Learn prepares you for the exam.
Why Track 1: Microsoft certification recognized globally. Validates AI vocabulary for business conversations. Gateway to Azure AI ecosystem. No technical prerequisites.
★ Google AI Essentials *
Track: Track 1 (Phase 1) Provider: Google / Coursera
URL: https://www.coursera.org/learn/google-ai-essentials
Duration: Under 10 hours Cost: $49/month Coursera subscription
Description: Taught by Google AI practitioners, this course covers generative AI tools, prompt engineering, and responsible AI use. Hands-on practice with AI tools across real workplace scenarios. Learn to use AI for brainstorming, research, content creation, and decision-making. Google certificate upon completion.
Why Track 1: Google brand recognition. Practical application focus. Fast completion. Updated content reflecting current AI capabilities.
★ Python for Everybody *
Track: Both Tracks (Track 1 Phase 2, Track 2 Phase 1) Provider: University of Michigan / Coursera
URL: https://www.coursera.org/specializations/python
Duration: ~8 months at 3 hours/week Cost: $49-98/month Coursera subscription
Description: Five-course specialization taught by Dr. Charles Severance covering Python basics, data structures, web data access, databases, and capstone project. Over 3 million enrollees. No prior programming experience required. Builds from absolute basics to working with APIs and databases. Practical projects throughout.
Why Both Tracks: Essential programming foundation for all AI work. Michigan credential. Dr. Severance’s teaching style accessible to complete beginners. Required before ML Specialization.
★ Mathematics for Machine Learning *
Track: Track 2 (Phase 1) Provider: Imperial College London / Coursera
URL: https://www.coursera.org/specializations/mathematics-machine-learning
Duration: ~16 weeks at 4 hours/week Cost: $49-98/month Coursera subscription
Description: Three-course specialization covering linear algebra, multivariate calculus, and principal component analysis. Taught by Imperial College faculty. Builds mathematical intuition for understanding ML algorithms at depth. Includes Python implementations of mathematical concepts. Prerequisites: basic algebra and trigonometry.
Why Track 2: Deep understanding of why algorithms work, not just how to use them. Imperial College London credential. Essential for engineering roles requiring algorithm modification or development.
★ Machine Learning Specialization *
Track: Both Tracks (Track 1 Phase 2, Track 2 Phase 2) Provider: Stanford / DeepLearning.AI / Coursera
URL: https://www.coursera.org/specializations/machine-learning-introduction
Duration: ~3 months at 8-10 hours/week Cost: $49-98/month Coursera subscription
Description: Three-course program taught by Andrew Ng. Updated version of the course taken by over 4.8 million learners since 2012. Covers supervised learning (regression, classification), neural networks, unsupervised learning, recommender systems, and reinforcement learning. Hands-on labs in Python using NumPy and TensorFlow.
Why Both Tracks: Industry gold standard. Andrew Ng’s teaching excellence. Stanford credential. Foundation for all subsequent deep learning work. Updated 2022 with modern tools and techniques.
★ Deep Learning Specialization *
Track: Both Tracks (Track 1 Phase 3 optional, Track 2 Phase 3 required) Provider: DeepLearning.AI / Coursera
URL: https://www.coursera.org/specializations/deep-learning
Duration: ~4 months at 5 hours/week Cost: $49-98/month Coursera subscription
Description: Five courses covering neural networks, hyperparameter tuning, regularization, optimization, convolutional networks, sequence models, and attention mechanisms. Taught by Andrew Ng. Build and train deep neural networks, implement CNNs for image recognition, and RNNs for sequence data. Capstone projects using TensorFlow.
Why Both Tracks: Deepens understanding beyond ML Specialization. Required for AI engineering roles. DeepLearning.AI credential. Covers architectures underlying modern AI systems.
★ Natural Language Processing Specialization *
Track: Track 2 (Phase 3) Provider: DeepLearning.AI / Coursera
URL: https://www.coursera.org/specializations/natural-language-processing
Duration: ~4 months at 6 hours/week Cost: $49-98/month Coursera subscription
Description: Four courses covering sentiment analysis, machine translation, question answering, and chatbots. Build sequence models, attention mechanisms, and transformer architectures. Hands-on with TensorFlow. Covers the foundational architectures behind GPT and BERT.
Why Track 2: NLP specialization increasingly valuable as LLMs dominate. Understanding transformer architecture essential for modern AI work. Prepares for LLM engineering roles.
★ Generative Adversarial Networks (GANs) Specialization *
Track: Track 2 (Phase 3) Provider: DeepLearning.AI / Coursera
URL: https://www.coursera.org/specializations/generative-adversarial-networks-gans
Duration: ~3 months at 8 hours/week Cost: $49-98/month Coursera subscription
Description: Three courses on GAN architectures, training techniques, and applications. Build GANs from scratch, implement StyleGAN, and explore image-to-image translation. Covers mode collapse, training stability, and evaluation metrics. Hands-on projects generating synthetic images.
Why Track 2: Generative AI depth beyond user-level prompting. Understanding generative model internals. Valuable for creative AI, synthetic data, and image generation roles.
★ Google Cloud Professional ML Engineer *
Track: Both Tracks (Phase 3/4 option) Provider: Google Cloud
URL: https://cloud.google.com/learn/certification/machine-learning-engineer
Duration: 2-hour exam, 50-60 questions Cost: $200 exam fee
Description: Validates ability to design, build, and productionize ML models on Google Cloud. Covers ML problem framing, data engineering, model development, ML pipelines, and MLOps. Requires 3+ years industry experience. Valid for two years. Demonstrates production-ready ML engineering capability.
Why Both Tracks: Enterprise cloud credential. Google brand. Proves ability to deploy ML at scale. Valuable for organizations using GCP.
★ Microsoft Azure AI Engineer Associate (AI-102) *
Track: Both Tracks (Phase 3/4 option) Provider: Microsoft Learn
URL: https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/
Duration: 20-30 hours preparation, exam Cost: $165 exam fee
Description: Validates ability to design and implement AI solutions using Azure AI services, Azure AI Search, and Azure OpenAI. Updated April 2025 with Azure OpenAI Service, RAG patterns, responsible AI design. Covers computer vision, NLP, conversational AI, and knowledge mining. Renew annually through free online assessment.
Why Both Tracks: Microsoft ecosystem dominance in enterprise. Updated for modern AI including Azure OpenAI. Production-focused certification.
★ AWS Machine Learning Engineer Associate *
Track: Both Tracks (Phase 3/4 option) Provider: Amazon Web Services
URL: https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/
Duration: 170 minutes exam Cost: $150 exam fee
Description: Newer associate-level certification focused on MLOps and production implementation. Covers data preparation, model development, deployment, and monitoring on AWS. Validates ability to build, train, deploy, and maintain ML solutions. Replaces retiring ML Specialty certification (retiring March 2026).
Why Both Tracks: AWS market leadership. MLOps focus reflects industry direction. More accessible than specialty certification. Current and maintained.
SUPPLEMENTARY COURSES
These courses extend beyond the core tracks for specialized learning, alternative credentials, or deeper expertise in specific domains.
FOR LEADERS & BUSINESS PROFESSIONALS
Grow with Google: Make AI Work for You
Provider: Google / Grow with Google
URL: https://grow.google/grow-your-business/
Duration: ~10 hours (self-paced) Cost: Free
Description: Hands-on playbook for non-technical leaders deploying AI in business operations. Expert-led training using Google AI tools including Gemini and Google Workspace. Real small business case studies from retail to finance. In-person workshops touring nationally through 2025. Guided practice solving real business problems with AI.
Value: Practical AI deployment for managers without technical background. Google tools integration. Free with workshop options.
Google AI for Anyone
Provider: Google / edX
URL: https://www.edx.org/learn/artificial-intelligence/google-google-ai-for-anyone
Duration: Self-paced Cost: Free
Description: Shorter format introduction to AI concepts taught by Laurence Moroney. Covers what AI is, how it works, and practical applications. No technical background required. Google credential upon completion.
Value: Alternative to Google AI Essentials for time-constrained learners. edX platform preference. Laurence Moroney’s teaching style.
AI For Everyone
Provider: DeepLearning.AI / Coursera
URL: https://www.coursera.org/learn/ai-for-everyone
Duration: 8 hours Cost: Free audit
Description: Non-technical AI course taught by Andrew Ng. Covers what AI can and cannot do, building AI projects, working with AI teams, and AI strategy. Designed for managers, executives, and non-engineers. No programming required. Over 1 million learners.
Value: Andrew Ng’s teaching excellence. Faster completion than Elements of AI. Business strategy focus.
Wharton: AI for Business Specialization
Provider: University of Pennsylvania / Coursera
URL: https://www.coursera.org/specializations/ai-for-business-wharton
Duration: 4 courses (~16 weeks) Cost: Free audit
Description: Four-course specialization from Wharton covering Big Data, machine learning fundamentals, AI in marketing (customer journey), and AI governance. Taught by Professor Kartik Hosanagar. Business school perspective on AI strategy and implementation.
Value: Wharton brand recognition. Business strategy context. MBA-style approach to AI. Governance coverage.
Stanford: AI Awakening – Implications for Economy and Society
Provider: Stanford Online
URL: https://online.stanford.edu/courses/soe-ycs0016-ai-awakening-implications-economy-and-society
Duration: Self-paced Cost: Free
Description: Non-technical exploration of AI’s economic and societal implications. Covers labor market disruption, inequality, privacy, autonomous systems, and governance challenges. Stanford faculty perspectives on AI risks and opportunities.
Value: Policy and strategy focus. Stanford credential. Essential context for executives and policymakers.
Harvard: Generative AI – How to Use It and Why It Matters
Provider: Harvard Kennedy School Executive Education
URL: https://www.exed.hks.harvard.edu/Programs/generative-ai
Duration: 6 weeks Cost: Paid (executive education pricing)
Description: Executive program from Harvard Kennedy School examining generative AI through policy lens. Covers applications, risks, governance frameworks, and strategic implementation. Live faculty sessions. Designed for government leaders and senior executives.
Value: Harvard Kennedy School credential. Policy framing. Government and public sector focus.
UC Berkeley: AI for Business
Provider: Berkeley Executive Education
URL: https://em-executive.berkeley.edu/artificial-intelligence-business-strategies
Duration: 6 weeks Cost: Paid (executive education pricing)
Description: Executive program featuring live faculty sessions with Professor Pieter Abbeel and Berkeley AI faculty. Covers AI strategy, implementation frameworks, and competitive advantage. Case studies from leading organizations.
Value: Berkeley credential. Pieter Abbeel access. Live interaction with leading AI researchers.
Oxford: Artificial Intelligence Programme
Provider: Oxford Saïd Business School
Duration: 6 weeks Cost: Paid (executive education pricing)
Description: Executive program covering AI fundamentals, applications, ethics, and strategic implementation. Multi-disciplinary Oxford faculty including computer science, philosophy, and business. Business case development. Ethical frameworks for responsible AI deployment.
Value: Oxford credential. European perspective. Ethics emphasis. Multi-disciplinary approach.
FOR ENGINEERS & TECHNICAL PRACTITIONERS
fast.ai: Practical Deep Learning for Coders
Provider: fast.ai
Duration: 7 weeks Cost: Free
Description: Code-first, theory-second approach to deep learning taught by Jeremy Howard and Rachel Thomas. Build working models from lesson one. Covers image classification, NLP, tabular data, and collaborative filtering. Uses fast.ai library built on PyTorch. Alumni working at Google Brain, OpenAI, Tesla.
Value: Highly respected practical approach. Jeremy Howard’s teaching. Industry recognition despite no university credential. Build immediately, understand later.
fast.ai: From Deep Learning Foundations to Stable Diffusion
Provider: fast.ai
URL: https://course.fast.ai/Lessons/part2.html
Duration: Self-paced Cost: Free
Description: Advanced course building diffusion models from scratch. Covers DDPM, DDIM, textual inversion, Dreambooth, and Stable Diffusion internals. Requires completion of Practical Deep Learning. Implementation-level understanding of generative AI.
Value: Deep generative AI understanding. Build Stable Diffusion from scratch. Advanced practitioner depth.
MIT 6.S191: Introduction to Deep Learning
Provider: MIT
URL: https://introtodeeplearning.com/
Duration: 1 week intensive (open-sourced annually) Cost: Free
Description: MIT’s introductory deep learning course, updated annually with latest developments. 2025 content includes diffusion models, LLM applications, and reinforcement learning. Guest lectures from Google, Microsoft Research. Covers neural networks, CNNs, RNNs, transformers, and generative models.
Value: MIT credential. Annual updates with cutting-edge content. Industry guest lecturers. Intensive format.
Stanford CS221: AI Principles and Techniques
Provider: Stanford
URL: https://stanford-cs221.github.io/
Duration: Full semester Cost: Free (materials)
Description: Stanford’s comprehensive AI course covering search, constraint satisfaction, Markov decision processes, game playing, machine learning, and knowledge representation. Full lecture videos, assignments, and exams available. Graduate-level depth.
Value: Stanford credential. Comprehensive AI foundations. Capstone after Track 2 completion.
Stanford CS229: Machine Learning
Provider: Stanford
URL: https://cs229.stanford.edu/
Duration: Full semester Cost: Free (materials)
Description: Andrew Ng’s original Stanford machine learning course. Graduate-level depth covering supervised learning, unsupervised learning, learning theory, and reinfortic learning. Full lecture notes, problem sets, and exams. More mathematical rigor than Coursera version.
Value: Original Andrew Ng course. Graduate-level mathematical depth. Self-directed learners.
Harvard CS50: Introduction to AI with Python
Provider: Harvard / edX
URL: https://cs50.harvard.edu/ai/
Duration: 7 weeks Cost: Free audit
Description: Harvard’s AI course covering search algorithms, knowledge representation, uncertainty, optimization, machine learning, and neural networks. Hands-on projects in Python. Part of CS50 family. Taught by David Malan and Brian Yu.
Value: Harvard credential. Single integrated course. CS50 quality. Good standalone AI option.
UC Berkeley CS188: Artificial Intelligence
Provider: UC Berkeley / edX
Duration: 12 weeks Cost: Free audit
Description: Berkeley’s AI course featuring Pacman-themed programming projects. Covers search, CSPs, game playing, MDPs, reinforcement learning, probability, and machine learning. Excellent visualizations and engaging assignments.
Value: Berkeley credential. Engaging Pacman projects. Strong graduate program preparation.
CMU: Machine Learning in Production
Provider: Carnegie Mellon
URL: https://mlip-cmu.github.io/
Duration: Full semester Cost: Free (materials on GitHub)
Description: Production-focused ML course covering building, deploying, and assuring ML products. Covers responsible AI including safety, security, and fairness. MLOps, testing, monitoring, and technical debt. Textbook and slides open-sourced on GitHub.
Value: MLOps depth. Production engineering focus. CMU credential. Open-source materials.
Google: Machine Learning Crash Course
Provider: Google
URL: https://developers.google.com/machine-learning/crash-course
Duration: 15 hours Cost: Free
Description: Google’s internal ML training made public. Updated 2024 with LLM content, AutoML, and responsible AI. Interactive visualizations, TensorFlow exercises, 130+ quiz questions. Covers regression, classification, neural networks, embeddings, and ML engineering.
Value: Google internal quality. Updated content. Interactive exercises. Free certification prep.
Google: Problem Framing
Provider: Google
URL: https://developers.google.com/machine-learning/problem-framing
Duration: ~2 hours Cost: Free
Description: Short course on deciding if ML is necessary for your problem. Covers problem identification, success metrics, data assessment, and build vs. buy decisions. Essential scoping before building.
Value: Often-overlooked skill. Prevents wasted effort. Strategic ML thinking.
Google: Testing and Debugging ML Models
Provider: Google URL: https://developers.google.com/machine-learning/testing-debugging
Duration: ~4 hours Cost: Free
Description: ML systems testing differs from traditional software testing. Covers data validation, model validation, A/B testing, debugging production issues, and monitoring. Production-focused quality assurance.
Value: Production debugging skills. Quality assurance focus. Complement to model building.
Google: Recommendation Systems
Provider: Google
URL: https://developers.google.com/machine-learning/recommendation
Duration: ~4 hours Cost: Free
Description: Build recommendation applications using collaborative filtering, content-based filtering, and deep learning approaches. Covers evaluation metrics, embedding spaces, and production considerations.
Value: Specialized ML application. High business value use case. Production patterns.
FOR BEGINNERS & CAREER CHANGERS
DeepLearning.AI: Agentic AI
Provider: DeepLearning.AI
URL: https://www.deeplearning.ai/courses/agentic-ai/
Duration: 5 modules (self-paced) Cost: Paid (DeepLearning.AI subscription)
Description: Andrew Ng teaches four design patterns powering autonomous AI: Reflection (AI critiques own work), Tool Use (connecting to APIs and databases), Planning (breaking complex tasks into steps), and Multi-Agent (coordinating specialized AI systems). Builds production-ready agentic applications from scratch in Python. Certificate provided.
Value: Cutting-edge agentic AI skills. Andrew Ng instruction. 2025’s most in-demand AI architecture.
NVIDIA: Generative AI Explained
Provider: NVIDIA Deep Learning Institute
URL: https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-07+V1
Duration: ~2 hours Cost: Free
Description: No-coding introduction to generative AI concepts. Covers what generative AI is, how it works, applications across industries, and challenges. Certificate available. Part of NVIDIA’s broader learning path ecosystem.
Value: Quick GenAI exposure. NVIDIA credential. No prerequisites. Fast completion.
Kaggle: Intro to Machine Learning
Provider: Kaggle
URL: https://www.kaggle.com/learn/intro-to-machine-learning
Duration: ~4 hours Cost: Free
Description: Fast, no-fluff introduction to ML. Browser-based coding, no installation required. Covers decision trees, random forests, model validation, and underfitting/overfitting. Build first model within hours.
Value: Immediate hands-on coding. No setup friction. Quick ML exposure before committing.
Kaggle: Intermediate Machine Learning
Provider: Kaggle
URL: https://www.kaggle.com/learn/intermediate-machine-learning
Duration: ~4 hours Cost: Free
Description: Covers missing values, categorical variables, pipelines, cross-validation, XGBoost, and data leakage. Competition-focused practical skills. Browser-based exercises.
Value: Competition-winning techniques. Practical data handling. Kaggle competition preparation.
Kaggle: Deep Learning
Provider: Kaggle
URL: https://www.kaggle.com/learn/intro-to-deep-learning
Duration: ~4 hours Cost: Free
Description: Build CNNs with TensorFlow and Keras. Covers neural network fundamentals, stochastic gradient descent, overfitting, dropout, batch normalization, and convolutional layers. Quick neural network exposure.
Value: Fast deep learning introduction. TensorFlow/Keras practice. Kaggle platform familiarity.
Google/Kaggle: Gen AI Intensive
Provider: Google & Kaggle
URL: https://blog.google/feed/kaggle-genai-intensive-course-2025/
Duration: 5 days (annual March event) Cost: Free
Description: Annual intensive event with 140,000+ participants in 2024. Covers prompt engineering, embeddings, foundation models, and fine-tuning. Certificate and prizes. Live community experience. Next cohort March 2025.
Value: Community learning experience. Certificate and recognition. Free intensive format.
IBM Introduction to Artificial Intelligence
Provider: IBM / Coursera
URL: https://www.coursera.org/learn/introduction-to-ai
Duration: Self-paced Cost: Free audit
Description: Covers AI concepts, applications, ethical considerations, and career opportunities. Hands-on projects using IBM Watson. IBM credential depth beyond SkillsBuild badges.
Value: IBM credential. Ethics coverage. Career guidance. Hands-on Watson experience.
OpenAI & DeepLearning.AI: ChatGPT Prompt Engineering
Provider: DeepLearning.AI
URL: https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
Duration: 2-3 weeks Cost: Free
Description: Taught by Isa Fulford (OpenAI) and Andrew Ng. Covers prompting principles, iterative development, summarizing, inferring, transforming, and building chatbots. API-based approach using Python.
Value: OpenAI expertise. Structured prompting approach. Free course. Good pre-governance preparation.
Vanderbilt: Prompt Engineering for ChatGPT
Provider: Vanderbilt University / Coursera
URL: https://www.coursera.org/learn/prompt-engineering
Duration: 6 modules Cost: Free audit
Description: Academic framing of prompt engineering from research university. Covers prompt patterns, personas, templates, and advanced techniques. Formal coursework structure.
Value: Academic credential. Structured curriculum. University teaching style.
FOR ETHICS & GOVERNANCE FOCUS
IAPP: Artificial Intelligence Governance Professional (AIGP)
Provider: International Association of Privacy Professionals
URL: https://iapp.org/certify/aigp/
Duration: ~15 hours prep + 2.75 hour exam (85 questions) Cost: ~$550 (training + exam)
Description: Industry gold standard for AI governance certification. Maps AI risk tiers to compliance with audit templates for bias and transparency. Updated February 2025 Body of Knowledge covers EU AI Act and emerging legislation. Demonstrates operational governance competency for privacy professionals, CISOs, and compliance roles.
Value: Industry-recognized certification. Regulatory compliance focus. Career credential for governance roles.
University of Helsinki: Ethics of AI
Provider: University of Helsinki
URL: https://ethics-of-ai.mooc.fi/
Duration: Self-paced Cost: Free
Description: Companion course to Elements of AI focusing on ethical considerations. Covers bias, transparency, accountability, privacy, and societal impact through practical cases. Same trusted provider and quality.
Value: Natural Elements of AI extension. Practical ethics cases. Free Helsinki credential.
BlueDot Impact: AI Safety Fundamentals
Provider: BlueDot Impact
URL: https://aisafetyfundamentals.com/
Duration: 8-10 weeks Cost: Free
Description: Theory-heavy course on AI alignment and safety. Used by Anthropic, OpenAI, UK AI Safety Institute for technical safety training. Covers alignment problem, specification, robustness, and governance. Strong for researchers targeting alignment roles.
Value: AI safety career preparation. Industry recognition. Alignment research focus.
Google Responsible AI Practices
Provider: Google
URL: https://ai.google/responsibility/responsible-ai-practices/
Duration: 3-4 weeks Cost: Free
Description: Google’s approach to responsible AI development. Covers fairness, interpretability, privacy, and safety. Best practices for building trustworthy AI systems. Google credential.
Value: Google perspective on responsible AI. Practical implementation guidance. Free credential.
UC Davis: Big Data, AI & Ethics
Provider: UC Davis / Coursera
URL: https://www.coursera.org/learn/big-data-ai-ethics
Duration: 4 weeks Cost: Free audit
Description: Academic treatment of data ethics covering privacy, consent, bias, and accountability. Policy-focused rather than implementation-focused. Good for compliance and policy roles.
Value: Academic credential. Policy perspective. Compliance role preparation.
MIT: Ethics of AI Bias
Provider: MIT OpenCourseWare
URL: https://ocw.mit.edu/courses/res-10-002-ethics-of-ai-bias-spring-2023/
Duration: Self-paced Cost: Free
Description: Deep dive into bias mechanisms in AI systems. Covers sources of bias, measurement, mitigation strategies, and case studies. MIT credential for auditors, compliance professionals, and DEI roles.
Value: MIT credential. Bias specialization. Free access.
fast.ai: Practical Data Ethics
Provider: fast.ai / USF Data Institute
Duration: Self-paced Cost: Free
Description: Real-world harm cases rather than abstract principles. Covers disinformation, bias, surveillance, algorithmic colonialism, and labor impacts. Taught by Rachel Thomas.
Value: Practical harm focus. Real cases. fast.ai teaching quality.
Université de Montréal: Bias and Discrimination in AI
Provider: Université de Montréal / edX
Duration: Self-paced Cost: Free audit
Description: Technical approach to bias mitigation requiring math and programming background. Covers gender, race, and socioeconomic bias in predictive models. Quantitative fairness metrics and mitigation techniques.
Value: Technical bias mitigation. Mathematical rigor. Research-oriented approach.
Kaggle: Intro to AI Ethics
Provider: Kaggle
URL: https://www.kaggle.com/learn/intro-to-ai-ethics
Duration: ~4 hours Cost: Free
Description: Quick introduction to AI ethics covering human-centered design, model cards, and fairness concepts. Browser-based exercises. Part of Kaggle Learn platform.
Value: Fast ethics introduction. Hands-on exercises. No prerequisites.
Linux Foundation: Ethics in AI and Data Science (LFS112x)
Provider: Linux Foundation
URL: https://training.linuxfoundation.org/training/ethics-in-ai-and-data-science-lfs112/
Duration: Self-paced Cost: Free
Description: Open source community approach to AI ethics. Covers data misuse impacts, algorithmic accountability, and critical thinking frameworks. Linux Foundation credential.
Value: Open source perspective. Community approach. Free credential.
FOR ADVANCED SPECIALIZATION
CMU: Generative AI & Large Language Models Certificate
Provider: Carnegie Mellon School of Computer Science
URL: https://www.cmu.edu/online/gai-llm/index.html
Duration: 9-12 months Cost: Paid
Description: Graduate certificate from CMU School of Computer Science. Covers LLM customization, fine-tuning, multimodal ML, and production deployment. Deep technical depth for advanced practitioners.
Value: CMU SCS credential. LLM engineering depth. Graduate-level rigor.
CMU: Managing AI Systems Certificate
Provider: Carnegie Mellon Heinz College
URL: https://www.cmu.edu/online/mais/index.html
Duration: 12 months Cost: Paid
Description: Graduate certificate for non-technical professionals deploying AI at enterprise scale. Covers AI strategy, governance, change management, and organizational implementation. Heinz College credential.
Value: Non-technical AI leadership. Enterprise focus. CMU Heinz credential.
MIT: Foundation Models and Generative AI
Provider: MIT xPRO URL: https://professional.mit.edu/course-catalog/professional-certificate-program-machine-learning-artificial-intelligence-0 Duration: Varies Cost: Paid
Description: Professional certificate covering latest foundation model research. MIT credential for professionals with education budgets. Cutting-edge content from MIT faculty.
Value: MIT credential. Latest research. Professional development credit.
MIT: Machine Learning with Python
Provider: MITx / edX
Duration: 15 weeks Cost: Free audit
Description: Part of MITx MicroMasters in Statistics and Data Science. Covers linear models, neural networks, and deep learning. Graduate credit pathway for those pursuing MIT credentials.
Value: MicroMasters credit. MIT credential. Graduate pathway.
Stanford: Statistical Learning with Python
Provider: Stanford Online
URL: https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning-python
Duration: Self-paced Cost: Free
Description: Statistical foundations for machine learning. Covers supervised learning, statistical modeling, and model selection. Free textbook (Introduction to Statistical Learning) accompanies course. Good mathematical foundation building.
Value: Statistical rigor. Free textbook. Stanford credential. Foundation building.
QUICK REFERENCE
Cost Summary
Completely Free:
- Elements of AI, IBM SkillsBuild, fast.ai courses, MIT 6.S191, Google ML courses, Kaggle micro-courses, BlueDot Impact, NVIDIA GenAI Explained, Helsinki Ethics, Stanford open courses
Free Audit (Certificate Paid):
- AI For Everyone, Wharton AI for Business, Harvard CS50 AI, UC Berkeley CS188, MIT ML with Python
Subscription-Based ($49-98/month):
- Python for Everybody, Math for ML, ML Specialization, Deep Learning Specialization, NLP Specialization, GANs Specialization
Certification Exams:
- Microsoft AI-900 ($99), Azure AI-102 ($165), AWS ML Associate ($150), Google Cloud ML ($200), IAPP AIGP (~$550)
Paid Programs:
- Oxford AI Programme, Berkeley AI for Business, CMU certificates, Harvard Executive Education
This catalog supplements the curated AI Learning Roadmaps at basilpuglisi.com/ai-learning-roadmaps-for-professionals. Track 1 (Leadership Path) and Track 2 (Engineering Path) remain the recommended structured learning sequences.
Last Updated: December 2025