AI Fluency Course

Learn the basics of artificial intelligence. This course is for absolute beginners. This course was created by learnaianywhere.org and released to support the goals of National AI Literacy Day. You can learn more at https://learnaianywhere.org and download the resources used. For more on AI Literacy Day, check out ailiteracyday.org or their YouTube playlist: https://www.youtube.com/watch?v=vihMGH6aqTY&list=PLGhqQNp1xV3McvqOTfJmX2mAuuZqOKMEs ❤️ Try interactive AI courses we love, right in your browser: https://scrimba.com/freeCodeCamp-AI (Made possible by a grant from our friends at Scrimba) ⭐️ Contents ⭐️ - 00:00 Welcome To The Course - 01:46 Prerequisite - 02:51 Symbol Key - 03:19 Lesson 1: What is AI - Objectives - 03:35 Lesson 1: What is AI - What Is AI AI or Not: https://learnaianywhere.org/courses/ai-literacy-for-everyone/ai-or-not - 05:39 Lesson 1: What is AI - How AI Can Help Us Recap Quiz: https://learnaianywhere.org/courses/ai-literacy-for-everyone/recap-quiz-1 - 07:14 Lesson 1: What is AI - Project Time - 09:06 Lesson 2: The Key Parts of Machine Learning - Objectives - 09:27 Lesson 2: The Key Parts of Machine Learning - What is Machine Learning - 12:01 Lesson 2: The Key Parts of Machine Learning - Neuropocket Tutorial - 14:39 Lesson 2: The Key Parts of Machine Learning - AI Tools Can Make Mistake Recap Quiz: https://learnaianywhere.org/courses/ai-literacy-for-everyone/recap-quiz-2 - 16:27 Lesson 2: The Key Parts of Machine Learning - Project Time - 17:20 Lesson 3: How Do Machines Train - Objectives - 18:06 Lesson 3: How Do Machines Train - The Describer Drawer Game - 19:51 Lesson 3: How Do Machines Train - The Describer Drawer Game Demonstration - 23:07 Lesson 3: How Do Machines Train - What Is An Algorithm - 25:37 Lesson 3: How Do Machines Train - The Human Learning Algorithm - 30:25 Lesson 3: How Do Machines Train - The Machine Learning Algorithm Recap Quiz: https://learnaianywhere.org/courses/ai-literacy-for-everyone/recap-quiz-3 - 38:05 Lesson 3: How Do Machines Train - Project Time - 39:07 Lesson 4: Can Machines Be Responsible - Objectives - 39:34 Lesson 4: Can Machines Be Responsible - Bearly A Dog Challenge - 40:14 Lesson 4: Can Machines Be Responsible - What is Bias - 44:06 Lesson 4: Can Machines Be Responsible - Who is Responsible - 46:50 Lesson 4: Can Machines Be Responsible - Data Privacy - 50:46 Lesson 4: Can Machines Be Responsible - Responsible AI Recap Quiz: https://learnaianywhere.org/courses/ai-literacy-for-everyone/recap-quiz-4 - 51:20 Lesson 4: Can Machines Be Responsible - Project Time

Learn how to use the OpenAI's Assistants API to build powerful AI assistants. In this course, we'll explore how to leverage the Assistants API by OpenAI to create dynamic, intelligent web apps using Streamlit. What we'll cover: 1️⃣ Function Calling with the API: Learn to seamlessly integrate the Assistants API into your applications, enabling advanced AI functionalities right at your fingertips. 2️⃣ Knowledge Retrieval: Discover how to use the API to extract information, answer questions, and make your applications smarter and more responsive. 3️⃣ Code Interpreter Capabilities: Dive into the API's ability to interpret and generate code, a game-changer for automating tasks and enhancing your app's capabilities. 4️⃣ LLM Fundamentals: Gain a solid understanding of Large Language Models (LLMs) and how they form the backbone of OpenAI's Assistants API. This module demystifies the technology and provides a foundation for advanced application development. The course features a series of hands-on projects and real-world examples to apply what you've learned. By the end of our session, you'll have the skills and confidence to build your own intelligent web apps using Streamlit and OpenAI's Assistants API. ⭐️ Code ⭐️ Personal trainer: https://github.com/pdichone/vincibits-personal-trainer-assistant Personal trainer: https://github.com/pdichone/vincibits-personal-trainer-assistant Study-Buddy: https://github.com/pdichone/vincibits-study-buddy-knwoledge-retrieval ✏️ Created by @vincibits Twitter (X): @buildappswithme ❤️ Try interactive AI courses we love, right in your browser: https://scrimba.com/freeCodeCamp-AI (Made possible by a grant from our friends at Scrimba) ⭐️ Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:01:02) What’s This Course About - What Will You Learn? ⌨️ (0:01:33) Pre-requisites ⌨️ (0:02:44) Python and Dev tools Set up ⌨️ (0:04:22) VS Code Installation ⌨️ (0:05:31) OpenAI Account - Generate an API Key ⌨️ (0:07:53) What is the Assistants API & Benefits - Comparison Between the Chat Completion API and the Assistants API ⌨️ (0:18:16) Assistants API Building Blocks ⌨️ (0:24:04) Creating an Assistants API - Manually (Personal Trainer) ⌨️ (0:38:20) Creating an Assistants API - In Code (Personal Trainer) ⌨️ (1:04:15) Build a News Summarizer Introduction: Function Calling - A Streamlit Application ⌨️ (1:25:39) Create an AssistantsManager Class For our News Summarizer ⌨️ (2:10:46) Using the AssistantManager Class to Create an Assistant and run it as a Streamlit App ⌨️ (2:28:23) Knowledge Bases Retrieval Tools - How it Works & Introduction to Embeddings ⌨️ (2:35:25) Build a Study Buddy Streamlit Application ⌨️ (3:22:24) Run the Study Buddy Streamlit Application and Test ⌨️ (3:29:27) Wrap up and Final Considerations. 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

Master OpenCode from scratch and learn how to build real-world AI applications using multiple models—without vendor lock-in or expensive subscriptions. This complete OpenCode course covers everything you need to go from beginner to advanced, including setup, model integration, agent workflows, and real project building. You’ll learn how to use OpenCode with tools like Ollama for running models locally, and OpenRouter for accessing multiple cloud models—all in a single workflow. We’ll also explore how to use powerful open models like Gemma 4 and optimize cost using model switching strategies. 🔥 What you’ll learn: OpenCode setup (Windows, Mac, Linux) Local AI with Ollama Cloud models via OpenRouter Multi-agent workflows (Plan vs Build mode) Cost optimization techniques Building real apps (REST APIs, UI apps, automation tools) Sessions, debugging, and workflow management 🎯 Who this is for: Developers & engineers AI enthusiasts Startup founders Anyone building AI-powered applications If you want full control over your AI stack—this is the course to start with.

This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full training stack of how the models are developed, along with mental models of how to think about their "psychology", and how to get the best use them in practical applications. I have one "Intro to LLMs" video already from ~year ago, but that is just a re-recording of a random talk, so I wanted to loop around and do a lot more comprehensive version. Instructor Andrej was a founding member at OpenAI (2015) and then Sr. Director of AI at Tesla (2017-2022), and is now a founder at Eureka Labs, which is building an AI-native school. His goal in this video is to raise knowledge and understanding of the state of the art in AI, and empower people to effectively use the latest and greatest in their work. Find more at https://karpathy.ai/ and https://x.com/karpathy Chapters 00:00:00 introduction 00:01:00 pretraining data (internet) 00:07:47 tokenization 00:14:27 neural network I/O 00:20:11 neural network internals 00:26:01 inference 00:31:09 GPT-2: training and inference 00:42:52 Llama 3.1 base model inference 00:59:23 pretraining to post-training 01:01:06 post-training data (conversations) 01:20:32 hallucinations, tool use, knowledge/working memory 01:41:46 knowledge of self 01:46:56 models need tokens to think 02:01:11 tokenization revisited: models struggle with spelling 02:04:53 jagged intelligence 02:07:28 supervised finetuning to reinforcement learning 02:14:42 reinforcement learning 02:27:47 DeepSeek-R1 02:42:07 AlphaGo 02:48:26 reinforcement learning from human feedback (RLHF) 03:09:39 preview of things to come 03:15:15 keeping track of LLMs 03:18:34 where to find LLMs 03:21:46 grand summary Links - ChatGPT https://chatgpt.com/ - FineWeb (pretraining dataset): https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1 - Tiktokenizer: https://tiktokenizer.vercel.app/ - Transformer Neural Net 3D visualizer: https://bbycroft.net/llm - llm.c Let's Reproduce GPT-2 https://github.com/karpathy/llm.c/discussions/677 - Llama 3 paper from Meta: https://arxiv.org/abs/2407.21783 - Hyperbolic, for inference of base model: https://app.hyperbolic.xyz/ - InstructGPT paper on SFT: https://arxiv.org/abs/2203.02155 - HuggingFace inference playground: https://huggingface.co/spaces/huggingface/inference-playground - DeepSeek-R1 paper: https://arxiv.org/abs/2501.12948 - TogetherAI Playground for open model inference: https://api.together.xyz/playground - AlphaGo paper (PDF): https://discovery.ucl.ac.uk/id/eprint/10045895/1/agz_unformatted_nature.pdf - AlphaGo Move 37 video: https://www.youtube.com/watch?v=HT-UZkiOLv8 - LM Arena for model rankings: https://lmarena.ai/ - AI News Newsletter: https://buttondown.com/ainews - LMStudio for local inference https://lmstudio.ai/ - The visualization UI I was using in the video: https://excalidraw.com/ - The specific file of Excalidraw we built up: https://drive.google.com/file/d/1EZh5hNDzxMMy05uLhVryk061QYQGTxiN/view?usp=sharing - Discord channel for Eureka Labs and this video: https://discord.gg/3zy8kqD9Cp Educational Use Licensing This video is freely available for educational and internal training purposes. Educators, students, schools, universities, nonprofit institutions, businesses, and individual learners may use this content freely for lessons, courses, internal training, and learning activities, provided they do not engage in commercial resale, redistribution, external commercial use, or modify content to misrepresent its intent.

In this full stack development course, you will learn how to create an application using MongoDb for the database, Java and Spring Boot for the backend, and React for the frontend. You will learn to develop a movie review application that will feature a separation of concerns between the client code and the server code. By implementing this loosely coupled architecture, these two parts (implemented using different technologies) can evolve in parallel and independently from one another. ✏️ Farhan Hasin Chowdhury teaches the backend section. ✏️ Gavin Lon teaches the frontend section. @GavinLon 💻 Backend Code: https://github.com/fhsinchy/movieist 💻 Frontend Code: https://github.com/GavinLonDigital/movie-gold-v1 🔗 Spring Initializr - https://start.spring.io/ 🔗 JDK Download Page: https://www.oracle.com/java/technologies/downloads/ 🔗 IntelliJ IDEA Download Page: https://www.jetbrains.com/idea/download/ 🔗 Postman Download Page: https://www.postman.com/downloads/ 🏗 MongoDB provided a grant to make this course possible. ❤️ Try interactive Full Stack courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Fullstack (Made possible by a grant from our friends at Scrimba) ⭐️ Contents ⭐️ ⌨️ (0:00:00) Course Introduction ⌨️ (0:01:48) JDK and IntelliJ IDEA Installation ⌨️ (0:06:13) MongoDB Atlas ⌨️ (0:20:35) Project Initialization ⌨️ (0:27:03) Project Structure ⌨️ (0:29:13) Running The Project ⌨️ (0:32:41) Writing The First Endpoint ⌨️ (0:35:33) Database Configuration ⌨️ (0:38:49) Installing Additional Dependencies ⌨️ (0:44:48) Setting Up Environment Variables ⌨️ (0:47:20) Movies and Reviews Endpoints ⌨️ (1:31:46) Testing ⌨️ (1:35:42) Backend Conclusion ⌨️ (1:36:45) Frontend Introduction ⌨️ (1:38:32) Create the React Project ⌨️ (1:43:49) Applying Bootstrap to our React application ⌨️ (1:52:47) Implementing the Use State and Use Effect Hooks ⌨️ (1:58:04) Create Home and Hero Component ⌨️ (2:06:56) Style the Carousel ⌨️ (2:15:21) Create Header Component (Navigation) ⌨️ (2:21:55) Create Trailer Component with react-player ⌨️ (2:28:46) Create Movie Reviews Functionality ⌨️ (2:34:43) Add and Get Reviews with HTTP Requests ⌨️ (2:40:33) Course Wrap Up 🎉 Thanks to our Champion and Sponsor supporters: 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Erdeniz Unvan 👾 Justin Hual 👾 Agustín Kussrow 👾 Otis Morgan -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

Visit sintra.ai/leonvanzyl or use my promo code leonvanzyl at checkout to receive limited time offer of 72% off on all plans 🚀 Free resources: source code, prompts, workflows + peer discussion https://skool.com/leonvanzyl 🧪 Agentic Labs: AI coding courses, live Q&A, weekly builder challenges, direct access to me + a serious builder community https://skool.com/agentic-labs OpenCode is the open source coding agent that is quickly becoming a serious alternative to Claude Code. In this opencode tutorial, you will learn how to install OpenCode, connect free AI models through OpenCode Zen or premium providers like OpenAI, and master advanced features including agent skills, sub agents, MCP servers, and background agents. ⏱️ CHAPTERS: 00:00 - OpenCode intro and overview 00:39 - Installing OpenCode in seconds 01:25 - Connecting providers and OpenCode Zen 02:20 - Free AI coding models walkthrough 03:46 - Scaffolding a Next js project 04:55 - Subscriptions vs API keys 06:00 - Connecting OpenAI and GPT 5 07:30 - Installing agent skills from skills sh 10:30 - AGENTS md memory file setup 13:00 - Sub agents and context window protection 15:30 - Design system for consistent UI 15:50 - Sponsor message 18:00 - Building the AI Project Planner app 22:30 - Saving plans for future sessions 24:30 - Creating custom sub agents 28:30 - Playwright MCP server setup 30:30 - Auto testing the app with browser MCP Business & sponsorship enquiries: leon.vanzyl@gmail.com #opencode #agenticcoding