6-12 Basic
Code.org - AI for Oceans
Topics: Supervised machine learning basics, training data, classification, bias in datasets, taught through block coding
Prerequisites: None; basic mouse/trackpad control helpful
6-12 Basic
Scratch (MIT Media Lab)
Topics: Block-based programming (loops, conditionals, variables, events), animation, interactive stories, AI extensions for image/sound classification
Prerequisites: None; basic reading and mouse/keyboard use
6-12 Basic
MIT Day of AI - Elementary Modules
Topics: AI literacy, what algorithms are and how they make decisions, intro to AI ethics and bias
Prerequisites: None
6-12 Intermediate
Machine Learning for Kids
Topics: Supervised learning workflow (collecting labeled data, training a model, testing accuracy), classification models for text/image/number recognition, integrating models into Scratch
Prerequisites: Comfort with Scratch block coding recommended; free IBM Cloud account (adult sign-up may be needed)
6-12 Basic
Teachable Machine (Google)
Topics: No-code model training for image, sound, and pose classification; how models generalize
Prerequisites: None; basic computer/web use only
13-17 Basic
MIT App Inventor
Topics: Visual/block-based Android app development, UI/UX basics, sensors and data storage, AI-powered app components (image classification)
Prerequisites: Prior block-coding experience (Scratch or Code.org) strongly recommended
13-17 Basic
MIT Day of AI - Middle/High School Modules
Topics: AI ethics (bias, fairness, data privacy), hands-on training of simple classifiers, generative AI and societal impact
Prerequisites: Basic computer literacy and reading comprehension
13-17 Basic
IBM SkillsBuild - AI Fundamentals
Topics: AI literacy, generative AI foundations, prompt engineering, job-readiness credentials
Prerequisites: None; free accounts available for students 13+
13-17 Intermediate
Khan Academy - Statistics & Probability
Topics: Probability, statistics, and algebra foundations that underpin later machine learning coursework
Prerequisites: Basic algebra
18-24 Basic
DeepLearning.AI - AI for Everyone
Topics: Non-technical AI literacy, generative AI capabilities and limitations, business applications of AI
Prerequisites: None; no coding required
18-24 Basic
DeepLearning.AI - AI Python for Beginners
Topics: Python coding fundamentals with AI-assisted coding tools
Prerequisites: None; built for first-time coders
18-24 Basic
Anthropic Academy - AI Fundamentals & Prompting
Topics: How large language models work (tokens, context windows), prompt engineering, chain-of-thought techniques
Prerequisites: None; accessible to non-coders
18-24 Intermediate
Codecademy - AI/Machine Learning Skill Path
Topics: Supervised/unsupervised learning, model evaluation, intro to neural networks
Prerequisites: Basic Python syntax and basic statistics (mean, median, distribution)
18-24 Intermediate
Kaggle Learn - Intro/Intermediate Machine Learning
Topics: Decision trees, random forests, handling missing data, cross-validation, feature engineering
Prerequisites: Completion of Kaggle's Python and Pandas courses (or equivalent experience)
18-24 Intermediate
Microsoft Learn - AI Fundamentals (AI-900)
Topics: Core machine learning concepts, Azure AI services, responsible AI principles
Prerequisites: None required; basic cloud computing familiarity helps
18-24 Advanced
freeCodeCamp - Machine Learning with Python
Topics: TensorFlow, neural networks, classification and regression basics
Prerequisites: Completion of freeCodeCamp's Python fundamentals (variables, functions, loops, basic data structures)
18-24 Advanced
Coursera - Machine Learning Specialization
Topics: Supervised learning, neural networks, decision trees, unsupervised learning (clustering, anomaly detection), recommender systems
Prerequisites: Basic Python helpful but not required; high-school-level algebra recommended
18-24 Advanced
edX - CS50's Introduction to AI with Python
Topics: Search algorithms, knowledge representation, machine learning, neural networks, natural language processing
Prerequisites: Completion of CS50 or equivalent Python proficiency strongly recommended
25-60 Advanced
DeepLearning.AI - Deep Learning Specialization
Topics: Neural network fundamentals, hyperparameter tuning, CNNs (computer vision), sequence models (RNNs, LSTMs, Transformers/NLP)
Prerequisites: Intermediate Python coding, basic linear algebra/calculus; completion of the Machine Learning Specialization recommended
25-60 Advanced
DeepLearning.AI - Agentic AI & RAG Short Courses
Topics: Agentic AI systems, retrieval-augmented generation (RAG), multimodal AI, LLM fine-tuning, MLOps
Prerequisites: Working Python proficiency; familiarity with using LLMs/APIs
25-60 Advanced
Fast.ai - Practical Deep Learning for Coders
Topics: Code-first computer vision, NLP, tabular data, and recommendation systems using PyTorch/fastai; model deployment
Prerequisites: At least one year of coding experience (Python strongly preferred); no advanced math required
25-60 Advanced
MIT OpenCourseWare - AI & Machine Learning Courses
Topics: 6.034 Artificial Intelligence, 6.036 Intro to Machine Learning, 6.S191 Deep Learning; supporting linear algebra, calculus, probability/statistics
Prerequisites: Solid programming background (Python); calculus and linear algebra required before ML/AI-specific courses
25-60 Advanced
Stanford Online - CS229/CS230/CS231n/CS224n
Topics: Machine learning theory, deep learning, computer vision, natural language processing (graduate/research level)
Prerequisites: Strong programming background; multivariable calculus, linear algebra, and probability theory formally required
25-60 Advanced
NVIDIA Deep Learning Institute
Topics: GPU-accelerated neural network training, computer vision, NLP, generative AI, LLM deployment, robotics (Isaac), MLOps
Prerequisites: Basic Python for fundamentals courses; working knowledge of deep learning concepts for advanced tracks
25-60 Advanced
Microsoft Learn - AI Engineer Associate Path
Topics: Azure Machine Learning, model training/deployment, Azure OpenAI Service, prompt engineering, copilot development
Prerequisites: Python or C# proficiency and foundational ML knowledge expected
25-60 Advanced
Anthropic Academy - Building with Claude (API/Agents)
Topics: API usage, tool use/function calling, agents, retrieval-augmented generation (RAG), responsible AI development
Prerequisites: Basic programming knowledge (Python or JavaScript/TypeScript); familiarity with REST APIs recommended
All Ages Basic
Kaggle Learn - Python Fundamentals
Topics: Core Python syntax as the entry point to the full Kaggle Learn AI/ML track (Pandas, ML, Deep Learning, NLP, Computer Vision courses build on this)
Prerequisites: None
6-12 Basic
AI4K12
Topics: The "Five Big Ideas in AI" (Perception, Representation & Reasoning, Learning, Natural Interaction, Societal Impact); supervised learning via teachable-machine style activities; intro AI ethics and bias.
Prerequisites: None; basic Scratch/block-coding familiarity helpful but not required.
18-24 Basic
Grow with Google - AI
Topics: What AI and generative AI are, how large language models work, responsible AI use, prompt writing, and using AI for productivity and job searching.
Prerequisites: None.
18-24 Intermediate
Google Machine Learning Crash Course
Topics: Linear/logistic regression, gradient descent, classification, ROC/AUC, feature engineering, neural networks, embeddings, fairness.
Prerequisites: Basic algebra; some Python.
25-60 Advanced
Hugging Face Learn
Topics: Transformers, tokenization, embeddings, fine-tuning, LLM apps.
Prerequisites: Intro deep learning and Python.
25-60 Advanced
Andrej Karpathy - Neural Networks: Zero to Hero
Topics: Backpropagation, autograd, GPT transformers, PyTorch, BPE.
Prerequisites: Strong Python, calculus, linear algebra.