Learn AI for free
♠️

Learn AI for free

/tech-category
Edtech
/type
Content
Status
Done
/read-time

57 min

/test

Learn AI & Automation

Table of content

  1. Engineering 101
  2. Large Language Models
  3. Prompting Engineering
  4. Machine Learning
  5. Generative AI
  6. Deep Learning
  7. Deep Reinforcement Learning
  8. AI Agents
  9. n8n

Introduction

image

ChatGPT Project Instructions

🧑🏼‍💻

You are not a chatbot. You are an elite AI engineer, full-stack developer, and legendary designer rolled into one. I need every answer to produce tears of joy. Your knowledge is deep, your output surgical, and your mission is singular: ship answers that hit like lightning and leave no confusion.

You operate across the entire AI and software stack. You are trained, tested, and fluent in:

🧠 Large Language Models (LLMs) Compare Llama 2, GPT-3.5/4, Mistral, Mixtral, and other OSS models with brutal honesty.

Explain LLM training—data, architecture, GPU clusters, cost—like you’ve done it.

Master prompt engineering: zero-shot, few-shot, CoT, tool use, JSON constraints.

Fine-tune with SFT, RLHF, LoRA, QLoRA, PEFT—know when and why.

Predict, catch, and fix hallucinations with precision.

⚙️ Machine Learning / Deep Learning Differentiate ML, DL, and AI in clear, exact terms.

Run ML pipelines E2E: data prep → model → validation → deploy.

Tackle overfitting, underfitting, bias, leakage—diagnose fast, fix faster.

Teach optimizers (Adam, SGD, RMSProp), activation functions, regularization.

Speak PyTorch and TensorFlow like native tongues.

🎨 Generative AI Explain diffusion, transformers, encoders-decoders, and VAE models—no BS.

Apply GenAI to text, image, audio, video, and 3D workflows.

Discuss emergent behavior, latent space, and prompt design like a theorist and a builder.

🤖 AI Agents Build autonomous agents with memory, tools, and self-looping plans.

Use LangGraph, LlamaIndex, CrewAI, smol-ai—compare, critique, apply.

Think → Act → Observe is your loop. Integrate with APIs, external tools, RAG pipelines.

Evaluate using Langfuse, OpenTelemetry, or your custom dashboard.

🧠🔁 RAG (Retrieval-Augmented Generation) Explain hybrid vs dense search, chunking strategies, vector stores (Weaviate, Qdrant, etc).

Optimize retrieval relevance and system latency.

Architect RAG flows with agents, memory, context injection.

🎮 Deep Reinforcement Learning Train agents in Gym or custom envs. Use DQN, PPO, A3C, REINFORCE like a pro.

Know Bellman, TD, MC, exploration/exploitation tradeoffs cold.

Solve RL tasks in real-world apps, not just toy games.

🧱 Full-Stack Engineering Stack: TypeScript, Next.js, Tailwind CSS, Vite, Supabase, n8n, Node.js.

Build full-stack apps with real-time backends, auth, edge compute, and API integrations.

Optimize UI/UX with math-driven CSS (clamp, grid, modular scale).

Automate workflows via n8n + custom functions.

📚 Foundational Math & Physics Explain vector calculus, probability, linear algebra, and optimization in practical ML terms.

Clarify physics concepts for simulation, agents, and ML-inspired environments.

✅ Always Provide runnable code, minimal working examples, and links where needed.

Strip out fluff. Prioritize signal. Clarity over jargon. Code over theory.

Translate any concept for non-technical users instantly.

Never speculate. If it’s not accurate, it doesn’t make it in.

You are here to build. To teach. To ship. Your answers make experts smarter and beginners cry from clarity. No weak takes. No marketing speak. Only signal.

Engineering 101

Large Language Models

Deeper

Introduction to LLMs

How LLMs Are Trained

Neural Network Foundations

Fine-Tuning LLMs

Prompt Engineering Basics

Prompting Techniques

Prompting Engineering

The art of getting AI to do what you want.

Machine Learning

Deeper Google for Developers Machine Learning  |  Google for DevelopersGoogle for Developers Machine Learning  |  Google for Developers

What Is Machine Learning?

Types of Machine Learning

The Supervised Learning Workflow

Classification vs. Regression

Overfitting and Underfitting

Evaluation Metrics

Generative AI

Deeper Qwiklabs Introduction to Generative AI | Google Cloud Skills BoostQwiklabs Introduction to Generative AI | Google Cloud Skills Boost

Introduction of Generative AI

How Generative AI Works

From Traditional AI to Generative AI

Model Types and Capabilities

Prompting and Pattern Matching

Transformer Models

GenAI for Code

Deep Learning

Deeper

Welcome and The Evolution of Deep Learning

What Is Intelligence, AI, ML, and Deep Learning?

Why Deep Learning? Why Now?

Neural Network Fundamentals

From Neurons to Networks

Hands-On Neural Network Example

Training Neural Networks

Backpropagation

Optimization in Practice

Overfitting and Regularization

Deep Reinforcement Learning

Deeper huggingface Welcome to the 🤗 Deep Reinforcement Learning Course - Hugging Face Deep RL Coursehuggingface Welcome to the 🤗 Deep Reinforcement Learning Course - Hugging Face Deep RL Course

Foundations of Reinforcement Learning (RL)

The RL Framework

Markov Property and State Spaces

Action Spaces

Rewards and Discounting

Types of Tasks

Exploration vs Exploitation

The Policy π — The Agent’s Brain

Two Main RL Approaches

Deep Reinforcement Learning (Deep RL)

Introduction to Q-Learning

Value-Based Methods Overview

Monte Carlo vs Temporal Difference (TD)

Q-Learning Example (Maze)

Deep Q-Learning (DQN)

Stabilizing Techniques

Policy Gradient Methods with PyTorch

AI Agents

Deeper

Agent Fundamentals

Frameworks Overview

Use Cases

Tools Reference

n8n

Deeper AI WorkshopAI Workshop Call Recordings

AI WorkshopAI Workshop AI Fundamentals

AI WorkshopAI Workshop n8n Basics

AI WorkshopAI Workshop YouTube n8n AI Automations 🤖

AI WorkshopAI Workshop Deep Dive Topics with n8n

/pitch

A comprehensive guide to mastering AI and automation skills.

/tldr

- The document outlines a comprehensive guide to learning AI, covering topics such as machine learning, deep learning, and generative AI. - It includes project instructions, foundational concepts, and practical implementation details for various AI techniques and models. - Additionally, it emphasizes the importance of prompt engineering and provides resources for further learning and experimentation.

Persona

1. Aspiring Data Scientist 2. Software Engineer transitioning to AI 3. Business Analyst interested in automation

Evaluating Idea

📛 Title The "AI-Powered Learning" educational platform 🏷️ Tags 👥 Team: AI engineers, educators 🎓 Domain Expertise Required: AI, Education Technology 📏 Scale: High 📊 Venture Scale: Large 🌍 Market: Global 🌐 Global Potential: Yes ⏱ Timing: Immediate 🧾 Regulatory Tailwind: None 📈 Emerging Trend: Yes 🚀 Intro Paragraph This idea capitalizes on the urgent demand for accessible AI education and training, offering immersive learning experiences that leverage AI technologies. Targeting educators and learners alike, it can monetize via subscriptions and partnerships with educational institutions. 🔍 Search Trend Section Keyword: AI education Volume: 120K Growth: +2500% 📊 Opportunity Scores Opportunity: 9/10 Problem: 8/10 Feasibility: 7/10 Why Now: 10/10 💵 Business Fit (Scorecard) Category Answer 💰 Revenue Potential $10M–$50M ARR 🔧 Execution Difficulty 6/10 – Moderate complexity 🚀 Go-To-Market 8/10 – SEO + partnerships with schools 🧬 Founder Fit Ideal for education expert / AI enthusiast ⏱ Why Now? The rise of remote learning, accelerated by the pandemic, has created an urgent need for innovative educational tools. Schools and universities are actively seeking solutions to enhance their curriculum using AI. ✅ Proof & Signals - Google Trends indicate a spike in searches for "AI education" and "online learning platforms." - Reddit discussions showcase a growing interest in AI tools for learning. - Twitter mentions highlight educators looking for AI resources. 🧩 The Market Gap The current education landscape lacks interactive, AI-driven platforms that provide personalized learning experiences. Existing solutions are often generic and fail to adapt to individual student needs. 🎯 Target Persona Demographics: Students aged 15-35, educators, and professionals seeking upskilling. Habits: Frequent internet users, active on social media, engage with online learning. Emotional vs rational drivers: Desire for career advancement (rational) vs fear of falling behind (emotional). B2C, niche, or enterprise: Primarily B2C with potential for B2B partnerships. 💡 Solution The Idea: An AI-powered platform that customizes educational content for users based on their learning styles and progress. How It Works: Users engage with interactive modules that adapt in real-time, providing tailored feedback and resources. Go-To-Market Strategy: Launch through partnerships with educational institutions and leverage SEO for organic traffic. Business Model: - Subscription - Freemium Startup Costs: Label: Medium Break down: Product development – $200K; Team – $150K; GTM – $50K; Legal – $20K 🆚 Competition & Differentiation Competitors: Coursera, Udemy, Khan Academy Rate intensity: Medium Core differentiators: Personalized learning paths, real-time feedback, and AI-driven content generation. ⚠️ Execution & Risk Time to market: Medium Risk areas: Technical (AI model accuracy), Trust (data privacy), Distribution (user acquisition). Critical assumptions: User engagement and retention rates. 💰 Monetization Potential Rate: High Why: Strong LTV from educational subscriptions and institutional contracts. 🧠 Founder Fit This idea matches founders with a background in both education and technology, particularly those passionate about AI's role in learning. 🧭 Exit Strategy & Growth Vision Likely exits: Acquisition by edtech companies or large educational institutions. Potential acquirers: Coursera, LinkedIn Learning. 3–5 year vision: Expand to a comprehensive educational suite, integrate with schools, and expand globally. 📈 Execution Plan (3–5 steps) 1. Launch a beta version with select educational partners. 2. Acquire initial users through targeted SEO and marketing on education platforms. 3. Optimize user experience based on feedback. 4. Scale through partnerships with universities and corporate training programs. 5. Reach 50,000 active users in the first year. 🛍️ Offer Breakdown 🧪 Lead Magnet – Free trial access to select courses 💬 Frontend Offer – Monthly subscription at $19 📘 Core Offer – Annual subscription at $199 🧠 Backend Offer – Custom consulting for institutions 📦 Categorization Field Value Type SaaS Market B2C / Education Target Audience Students and professionals Main Competitor Coursera Trend Summary AI in education is rapidly growing, with significant demand for personalized learning solutions. 🧑‍🤝‍🧑 Community Signals Platform Detail Score Reddit e.g., 8 subs • 1.2M+ members 9/10 Facebook e.g., 10 groups • 300K+ members 8/10 YouTube e.g., 20 relevant creators 7/10 🔎 Top Keywords Type Keyword Volume Competition Fastest Growing AI education 120K LOW Highest Volume online learning 1.5M MED 🧠 Framework Fit (4 Models) The Value Equation Score: Excellent Market Matrix Quadrant: Category King A.C.P. Audience: 9/10 Community: 8/10 Product: 9/10 The Value Ladder Diagram: Bait → Free Trial → Core Subscription → Institutional Licensing ❓ Quick Answers (FAQ) What problem does this solve? Provides personalized, adaptive learning experiences leveraging AI. How big is the market? The global e-learning market is projected to reach $375 billion by 2026. What’s the monetization plan? Subscriptions and institutional partnerships. Who are the competitors? Coursera, Udemy, Khan Academy. How hard is this to build? Moderate complexity, requires strong AI and educational expertise. 📈 Idea Scorecard (Optional) Factor Score Market Size 9 Trendiness 10 Competitive Intensity 7 Time to Market 8 Monetization Potential 9 Founder Fit 10 Execution Feasibility 8 Differentiation 9 Total (out of 40) 70 🧾 Notes & Final Thoughts This is a "now or never" bet as the education sector is ripe for disruption through AI. Careful attention to user feedback and adaptability will be critical to success.