AI Technology | Claude & ChatGPT Prompts

🚀 7 Best Resources to Learn About Self-Evolving AI Agents

1. Hugging Face Agents Course

Learn AI agent fundamentals, tool use, reasoning, and agent frameworks.

🔗 https://huggingface.co/learn/agents-course

2. Stanford CS329A: Self-Improving AI Agents

Explore academic lectures and research on how AI agents can improve themselves.

🔗 https://cs329a.stanford.edu/

3. A Comprehensive Survey of Self-Evolving AI Agents

Understand how AI agents evolve their models, memory, prompts, and tools.

🔗 https://arxiv.org/abs/2508.07407

4. Self-Improvements in Modern Agentic Systems: A Survey

Discover different approaches to self-improvement in modern AI agent systems.

🔗 https://arxiv.org/abs/2607.13104

5. Awesome Self-Improving Modern Agentic Systems

Explore a curated collection of research papers, benchmarks, tools, and projects.

🔗 https://github.com/selfimproving-agent/Awesome-Self-Improving-Agents

6. Awesome RSI (Recursive Self-Improvement)

Explore research on recursive self-improvement, AI research automation, and safety.

🔗 https://github.com/lobehub/awesome-rsi

7. Awesome Harness Engineering for Self-Improvement

Learn how tools, memory, evaluation, and control loops help AI agents improve.

🔗 https://github.com/leezythu/Awesome-Harness-Self-Improvement

📌 Save this list if you're interested in building smarter, self-improving AI agents!

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🚀 5 Free Courses to Learn AI Engineering

1️⃣ Hugging Face LLM Course 🤗Learn Transf
ormers, tokenization, datasets, fine-tuning, and modern LLM workflows. Great for building a strong LLM foundation.

🔗 https://huggingface.co/learn/llm-course

2️⃣ AI Engineer Notebooks 💻Hands-on noteboo
ks covering RAG, AI agents, tool calling, evaluations, fine-tuning, LLMOps, and production AI engineering.

🔗 https://github.com/calmrocks/ai-engineer-notebooks

3️⃣ DataTalksClub LLM Zoomcamp 🧠Learn to build pract
ical LLM applications with RAG, vector search, agents, evaluation, monitoring, and production workflows.

🔗 https://github.com/DataTalksClub/llm-zoomcamp

4️⃣ MLOps Zoomcamp ⚙

️Learn how to take ML mod
els from development to production with MLflow, pipelines, deployment, monitoring, CI/CD, and more.

🔗 https://github.com/DataTalksClub/mlops-zoomcamp

5️⃣ Maxime Labonne's LLM Course 🔥Go deeper into open-source L
LMs, fine-tuning, QLoRA, quantization, model merging, inference optimization, and deployment.

🔗 https://github.com/mlabonne/llm-course

📚 All 5 are free — save this post and start learning!

#AI #AICourses #AIEngineering #LLM #MachineLearning #RAG #AIAgents #MLOps

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5 Free LLM API Providers You Can Use in 2026

1️⃣ GroqCloud
Get access to powerful LLMs with extremely fast inference. Great for chatbots, AI apps, and agentic projects.

👉 Click here: https://console.groq.com/home

2️⃣ OpenRouter
Try multiple AI models through one OpenAI-compatible API. Useful for comparing different free models without managing multiple providers.

👉 Click here: https://openrouter.ai/

3️⃣ Cloudflare Workers AI
Run AI models through Cloudflare's serverless platform with a free daily inference allowance. Great for building AI-powered web apps.

👉 Click here: https://developers.cloudflare.com/workers-ai/

4️⃣ Mistral
Get free monthly API credits and experiment with Mistral's models, API, Studio, and Vibe Code.

👉 Click here: https://mistral.ai/

5️⃣ Google Gemini API
Use Google's powerful Gemini models through the API with a Free Tier. Great for coding, multimodal AI, and AI agent projects.

👉 Click here: https://ai.google.dev/

#AI #LLM #FreeAI #APIs #Developers #GenerativeAI

🔥 5 Free Courses to Go From LLM Beginner to Practitioner

Want to go from LLM beginner → understanding → building → fine-tuning → deploying AI agents?
Here are 5 free courses to follow 👇

1️⃣ Neural Networks: Zero to Hero — Andrej Karpathy
Learn neural networks and language models from scratch. Build micrograd, language models, GPT, and tokenizers with hands-on coding.
🔗 Course: https://karpathy.ai/zero-to-hero.html

2️⃣ Full Stack LLM Bootcamp — Full Stack Deep Learning
Learn how to build production-ready LLM applications, including prompting, evaluation, LLMOps, deployment, and system design.
🔗 Course: https://fullstackdeeplearning.com/llm-bootcamp/

3️⃣ Stanford CS336 — Language Modeling from Scratch
Go deeper into how LLMs are actually built — data processing, tokenization, transformers, training, scaling, evaluation, and alignment.
🔗 Course: https://cs336.stanford.edu/

4️⃣ Hugging Face LLM Course
Learn practical LLM development with Transformers, Datasets, tokenization, fine-tuning, LoRA, SFT, DPO, and GRPO.
🔗 Course: https://huggingface.co/learn/llm-course

5️⃣ DeepLearning.AI — AI & LLM Short Courses
Learn practical topics like AI agents, LangGraph, RAG, model serving, and production AI workflows.
🔗 Courses: https://www.deeplearning.ai/courses/

📌 Save this post for later!

🔥 𝐓𝐨𝐩 𝟏𝟐 𝐀𝐈 𝐆𝐢𝐭𝐇𝐮𝐛 𝐑𝐞𝐩𝐨𝐬𝐢𝐭𝐨𝐫𝐢𝐞𝐬 𝐭𝐨 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐢𝐧 𝟐𝟎𝟐𝟔

1️⃣ OpenClaw — Build personal AI agents that can run on your device.
🔗 https://github.com/openclaw/openclaw

2️⃣ n8n — Build powerful visual automation workflows with AI.
🔗 https://github.com/n8n-io/n8n

3️⃣ Ollama — Run LLMs locally and experiment with open models.
🔗 https://github.com/ollama/ollama

4️⃣ Langflow — Build AI applications and agent workflows visually.
🔗 https://github.com/langflow-ai/langflow

5️⃣ Dify — Create and deploy production-ready AI applications.
🔗 https://github.com/langgenius/dify

6️⃣ LangChain — Build applications powered by LLMs, tools, and agents.
🔗 https://github.com/langchain-ai/langchain

7️⃣ Open WebUI — Self-hosted interface for interacting with AI models.
🔗 https://github.com/open-webui/open-webui

8️⃣ DeepSeek-V3 — Explore DeepSeek’s open AI model ecosystem.
🔗 https://github.com/deepseek-ai/DeepSeek-V3

9️⃣ Gemini CLI — Work with Gemini directly from your terminal.
🔗 https://github.com/google-gemini/gemini-cli

🔟 RAGFlow — Explore advanced RAG workflows for real-world applications.
🔗 https://github.com/infiniflow/ragflow

1️⃣1️⃣ Claude Code — Explore agentic AI coding workflows.
🔗 https://github.com/anthropics/claude-code

1️⃣2️⃣ CrewAI — Build multi-agent systems where specialized agents collaborate.
🔗 https://github.com/crewAIInc/crewAI

📌 Save this list and pick one repository to explore today.

#AI #GitHub #AIAgents #LLM #RAG #GenerativeAI #MachineLearning #OpenSource

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5 FREE AI Courses You Should Know About

1️⃣ Introduction to AI for Work — DataCamp

New to AI? This beginner-friendly course explains AI, machine learning, Generative AI, and LLMs with a focus on practical workplace use. No technical background required!
👉 Best for: Students, managers, marketers & beginners

🔗 Click Here: https://www.datacamp.com/courses/introduction-to-ai-for-work

2️⃣ Easy-Vibe AI Coding Guide from Scratch

Want to build apps with AI? Learn vibe coding, frontend, backend, databases, deployment, AI agents, and AI coding tools through a practical, project-focused approach.
👉 Best for: Beginners, founders, creators & product managers

🔗 Click Here: https://datawhalechina.github.io/easy-vibe/en/

3️⃣ LLM Course — Maxime Labonne

A powerful free roadmap for learning Large Language Models. Explore LLM fundamentals, fine-tuning, quantization, evaluation, deployment, and LLM engineering.
👉 Best for: Developers, ML learners & AI builders

🔗 Click Here: https://github.com/mlabonne/llm-course

4️⃣ LLM Zoomcamp — DataTalks.Club

Learn by building real-world LLM applications! Cover RAG, vector search, embeddings, AI agents, function calling, evaluation, monitoring, and reranking.
👉 Best for: Software engineers, data engineers & ML learners

🔗 Click Here: https://github.com/DataTalksClub/llm-zoomcamp

5️⃣ Hugging Face LLM Course

Learn LLMs and NLP using the Hugging Face ecosystem. Explore Transformers, Datasets, Tokenizers, fine-tuning, model sharing, and advanced LLM concepts.
👉 Best for: AI developers & learners interested in open-source LLMs

🔗 Click Here: https://huggingface.co/learn/llm-course/chapter1/1

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🔥 10 YouTube Channels Keeping You Ahead in AI

1️⃣ Two Minute Papers
Complex AI research explained in simple, visual, and exciting videos. Perfect for discovering the latest breakthroughs.

👉 Click Here: Two Minute Papers

2️⃣ Yannic Kilcher
Want to understand AI papers in depth? Yannic breaks down models, mathematics, architectures, and research methodology.

👉 Click Here: Yannic Kilcher

3️⃣ AI Jason
Learn how to build practical AI applications, AI agents, RAG systems, and multi-agent workflows with real-world examples.

👉 Click Here: AI Jason

4️⃣ AssemblyAI
A great resource for developers building with LLMs, speech AI, RAG, vector databases, and modern AI APIs.

👉 Click Here: AssemblyAI

5️⃣ Sentdex
Learn Python, Machine Learning, Deep Learning, and AI by actually building projects from the ground up.

👉 Click Here: Harrison Kinsley's Sentdex

6️⃣ Andrej Karpathy
Learn AI from first principles with one of the most respected AI engineers. His Neural Networks: Zero to Hero series is a must-watch.

👉 Click Here: Andrej Karpathy

7️⃣ StatQuest
Confused by statistics or Machine Learning? StatQuest makes difficult concepts simple, visual, and easy to remember.

👉 Click Here: StatQuest with Josh Starmer

8️⃣ DeepLearning.AI
Learn Machine Learning, Deep Learning, Generative AI, and AI concepts through structured educational content.

👉 Click Here: DeepLearning.AI

9️⃣ AI Explained
Stay updated on new AI models, benchmarks, research, and industry developments without the hype.

👉 Click Here: AI Explained

🔟 Matt Wolfe
Discover the latest AI tools, apps, startups, automation platforms, and productivity tools before they become mainstream.

👉 Click Here: Matt Wolfe

#AI #ArtificialIntelligence #MachineLearning #ChatGPT #GenerativeAI #LLM #AIAgents #DeepLearning #Python #AITools


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AI Technology | Claude & ChatGPT Prompts pinned «🚀 5 FREE Resources to Master Agentic AI 📘 Microsoft AI Agents for Beginners Learn AI agents, RAG, MCP, memory, and multi-agent systems with hands-on Python examples. 👉 Click Here: https://github.com/microsoft/AI-For-Beginners/tree/main/12-building-ai-agents…»

5 Free Courses to Go From AI Beginner to Practitioner

1️⃣ Harvard CS50: Introduction to AI with Python
🎓 Learn the fundamentals of AI before diving into machine learning. Build AI projects like Tic-Tac-Toe, search algorithms, and logic solvers while mastering core AI concepts.

👉 Click Here: https://cs50.harvard.edu/ai/

2️⃣ Google Machine Learning Crash Course
📊 Google's official ML course teaches gradient descent, TensorFlow, feature engineering, and model training with interactive lessons used by Google engineers.

👉 Click Here: https://developers.google.com/machine-learning/crash-course

3️⃣ fast.ai – Practical Deep Learning for Coders
💻 Build real deep learning models from the very first lesson. Learn computer vision, NLP, PyTorch, and deploy AI applications with practical projects.

👉 Click Here: https://course.fast.ai/

4️⃣ Hugging Face NLP Course
🤖 Master Transformers, LLMs, and modern Generative AI. Learn to fine-tune open-source models using the Hugging Face ecosystem.
👉 Click Here: https://huggingface.co/learn/nlp-course

5️⃣ Andrej Karpathy – Neural Networks: Zero to Hero
🧠 Build neural networks and a mini GPT completely from scratch. One of the best free resources to deeply understand how LLMs actually work.
👉 Click Here: https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ

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🚀 5 FREE Resources to Master Agentic AI

📘 Microsoft AI Agents for Beginners
Learn AI agents, RAG, MCP, memory, and multi-agent systems with hands-on Python examples.
👉 Click Here: https://github.com/microsoft/AI-For-Beginners/tree/main/12-building-ai-agents

🤗 Hugging Face AI Agents Course
Build real-world AI agents using LangGraph, LlamaIndex, and other popular frameworks.
👉 Click Here: https://huggingface.co/learn/agents-course

🧠 Anthropic – Building Effective Agents
Learn proven agent design patterns, workflows, routing, and evaluation strategies.
👉 Click Here: https://www.anthropic.com/engineering/building-effective-agents

📚 Multiagent Systems (Free Book)
Understand the theory behind agent coordination, negotiation, and decision-making.
👉 Click Here: https://www.masfoundations.org

🔍 Google & Kaggle Agents Whitepaper Series
Learn agent architectures, MCP, memory, evaluation, and production deployment.
👉 Click Here: https://www.kaggle.com/whitepaper-agents

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Best YouTube Channels to learn AI in 2026

1. AI Explained
👉 http://youtube.com/@aiexplained-o…

2. Andrej Karpathy
👉 https://www.youtube.com/@AndrejKarpathy

3. Cole Medin
👉 http://youtube.com/@WesRoth/

4. DeepLearningAI
👉 http://youtube.com/@Deeplearninga…

5. Futurepedia
👉 http://youtube.com/@futurepedia_i…

6. Matthew Berman
👉 http://youtube.com/@matthew_berma…

7. Skill Leap AI
👉 http://youtube.com/@SkillLeapAI/f…

8. Tech With Tim
👉 http://youtube.com/@TechWithTim/v…

9. Tina Huang
👉 http://youtube.com/@TinaHuang1/vi…

10. Two Minute Papers
👉 http://youtube.com/@TwoMinutePape


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🚀 7 Real-World Python Projects You Can Build in 2026

1. AI Scam & Notice Checker
Detect scam SMS, phishing messages, and fake notices with AI.

📖 https://huggingface.co

2. Multi-Agent Research Assistant
Build AI agents that research the web and generate reports.

📖 https://machinelearningmastery.com

3. Breast Cancer Prediction API
Train an ML model and deploy it with FastAPI.

📖 https://machinelearningmastery.com

4. AI Market Research Dashboard
Automate market research and trend analysis using AI.

📖 https://www.olostep.com/blog/agentic-market-research-olostep

5. Recycling Data Analysis
Analyze recycling data and create insightful visualizations.

📖 https://towardsdatascience.com

6. AI Resume & Job Match Analyzer
Match resumes with jobs and identify skill gaps.

📖 https://www.datacamp.com/tutorial/kimi-k2-6-api-tutorial

7. AI Data Analysis Report Generator
Generate charts, insights, and reports from datasets with AI.

📖 https://www.datacamp.com/tutorial/gemini-3-api-tutorial


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7 Best Small Language Models Under 10B Parameters in 2026

1. IBM Granite 4.1 8B

With industry-leading coding performance and a massive context window, Granite 4.1 8B is built for enterprise applications, RAG systems, and tool-calling workflows.

🔗 Click Here: https://huggingface.co/ibm-granite/granite-4.1-8b-instruct

2. Qwen3.5-9B

One of the strongest reasoning models under 10B parameters, Qwen3.5-9B excels in multilingual tasks, science QA, and advanced problem-solving.

🔗 Click Here: https://huggingface.co/Qwen/Qwen3.5-9B-Instruct

3. Gemma 4 E4B

Google's Gemma 4 E4B is optimized for AI agents, tool calling, and edge deployment, delivering powerful performance with minimal hardware requirements.

🔗 Click Here: https://huggingface.co/google/gemma-4-e4b-it

4. Qwen3-8B

A proven favorite for developers, Qwen3-8B offers excellent code generation capabilities and supports more than 29 languages.

🔗 Click Here: https://huggingface.co/Qwen/Qwen3-8B

5. DeepSeek-R1-Distill-Qwen-7B

Built for mathematical reasoning and logical thinking, this compact model delivers exceptional step-by-step problem-solving performance.

🔗 Click Here: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

6. Phi-4-mini

Microsoft's Phi-4-mini packs impressive AI capabilities into just 3.8B parameters, making it ideal for laptops and low-resource environments.

🔗 Click Here: https://huggingface.co/microsoft/Phi-4-mini-instruct

7. Llama 3.1 8B Instruct

Llama 3.1 8B remains one of the most versatile open-source models, backed by a massive ecosystem of fine-tunes and community support.

🔗 Click Here: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct

🔥 Which small LLM is your favorite in 2026?

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5 Fun Papers That Explain LLMs Clearly

1️⃣ Attention Is All You Need

📝 Description: Introduced the Transformer, the architecture behind every modern LLM. Replaced older recurrent/convolutional models for sequences.
🔑 Key Ideas: Self-attention • Multi-head attention • Positional encoding • Transformer block
🔗 Paper: https://arxiv.org/abs/1706.03762
━━━━━━━━━━━━━━━

2️⃣ Language Models Are Few-Shot Learners
📝 Description: The GPT-3 paper. One 175B model handles many tasks just by reading prompts — no retraining.
🔑 Key Ideas: In-context learning • Few-shot prompting • Autoregressive next-token prediction
🔗 Paper: https://arxiv.org/abs/2005.14165
━━━━━━━━━━━━━━━

3️⃣ Scaling Laws for Neural Language Models
📝 Description: Showed model performance improves predictably as parameters, data & compute grow. The logic behind going big.
🔑 Key Ideas: Scaling laws • Compute-optimal training • Data vs. model size tradeoffs
🔗 Paper: https://arxiv.org/abs/2001.08361
━━━━━━━━━━━━━━━

4️⃣ Training LMs to Follow Instructions with Human Feedback
📝 Description: The InstructGPT paper. Turns a raw text predictor into a helpful, instruction-following assistant.
🔑 Key Ideas: RLHF • Supervised fine-tuning • Reward model • Human preference ranking
🔗 Paper: https://arxiv.org/abs/2203.02155
━━━━━━━━━━━━━━━

5️⃣ Retrieval-Augmented Generation (RAG)
📝 Description: LLMs fetch external documents instead of relying only on stored memory — great for facts that change over time.
🔑 Key Ideas: Dense retrieval • Document index • Grounded generation • Knowledge-intensive QA
🔗 Paper: https://arxiv.org/abs/2005.11401

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5 Must-Know Python Concepts for AI Engineers

1. 🔥 Tensors & Autograd

Stop writing backprop by hand. requires_grad=True tracks every operation → .backward() applies the chain rule automatically.

import torch

x = torch.tensor(2.0)
y = torch.tensor(5.0)
w = torch.tensor(0.5, requires_grad=True)
b = torch.tensor(0.1, requires_grad=True)

pred = w * x + b
loss = (pred - y) ** 2
loss.backward()

print(w.grad.item(), b.grad.item())

✅ Exact gradients, zero math errors.

2. ⚙️ The __call__ Method

Why model(x) works, not model.forward(x). call runs hooks before forward.

class LinearLayer:
def __init__(self, w, b):
self.w, self.b = w, b
self._hooks = []

def __call__(self, x):
for hook in self._hooks:
hook(x)
return self.forward(x)

def forward(self, x):
return x * self.w + self.b

⚠️ Always call model(x) — .forward() skips hooks → silent bugs.

3. 💾 Pickle vs ONNX

pickle = Python-locked + code execution risk 🚨. ONNX = static, language-agnostic graph.

import torch

model.eval()
dummy_input = torch.randn(1, 10)

torch.onnx.export(
model, dummy_input, "model.onnx",
export_params=True,
opset_version=15,
input_names=["input"],
output_names=["output"],
dynamic_axes={"input": {0: "batch_size"}}
)

✅ Portable, fast, decoupled from training code.

4. 🧱 Abstract Base Classes

@abstractmethod forces subclasses to implement methods. Miss one → fails at startup, not mid-request.

from abc import ABC, abstractmethod

class ModelInterface(ABC):
@abstractmethod
def predict(self, x: list) -> list: ...

@abstractmethod
def get_metadata(self) -> dict: ...

✅ Fail fast, fail safe.

5. 🔐 Env Variables & Secrets

Never hardcode keys. Store in .env, gitignore it, load with python-dotenv.

import os
from dotenv import load_dotenv

load_dotenv()

api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise ValueError("OPENAI_API_KEY is not set!")

✅ Same code locally + Docker/Lambda. Zero leaks.

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7 Real World AI Projects to Build in 2026

🤖 Build an AI Job Search Assistant

Searching for jobs is repetitive — JobFit AI reads your CV, searches live postings, and generates a ranked job-fit report automatically.

📖 Guide: Kimi K2.6 API Tutorial
🐙 GitHub: kingabzpro/JobFit-AI

🔬 Build a Multi-Agent Research Assistant

Most research workflows involve several steps — this multi-agent system handles web search, source filtering, and report writing all in one pipeline.

📖 Guide: Multi-Agent Research Assistant in Python
🐙 GitHub: Multi-Agent-Research-Assistant

📈 Automate Investment Research with Olostep and n8n

Investment research means checking news, financials, and public sources — this workflow automates the entire process and delivers AI-generated reports.

📖 Guide: How to Automate Investment Research Using Olostep and n8n
🐙 GitHub: kingabzpro/olostep-n8n-investment-agent

📊 Build an Agentic Market Research and Trend Analysis App

Manually collecting competitor updates and trend reports takes hours — this agentic pipeline handles research, extraction, and brief writing automatically.

📖 Guide: Agentic Market Research & Trend Analysis with Olostep
🐙 GitHub: kingabzpro/agentic-market-research-olostep

🧾 Build an AI Invoice Processing Pipeline

Invoice processing combines document understanding and structured extraction — this pipeline uses vision AI to pull useful fields and output clean structured data.

📖 Guide: Qwen 3.6 Plus API Tutorial
🐙 GitHub: BexTuychiev/qwen-invoice-pipeline-tutorial

📉 Build a Chart Digitizer with Claude Opus 4.7

Visual data trapped inside static charts and PDFs is now extractable — this tool reads chart images and saves the data points into a clean CSV or DataFrame.

📖 Guide: Building a Chart Digitizer

🏋️ Build an Exercise Trainer with Persistent Memory

Most AI agents forget everything after a session — this exercise trainer remembers your workout history and suggests personalized sessions every time you run it.

📖 Guide: Add Persistent Memory to AI Agents

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10 Python Libraries for Building LLM Applications

🔹 1. Transformers
Core library for loading, fine-tuning, and running LLMs with ease.
👉 Learn more: https://huggingface.co/docs/transformers

🔹 2. LangChain
Connect prompts, tools, APIs, and models into powerful workflows.
👉 Learn more: https://docs.langchain.com

🔹 3. LlamaIndex
Bring your own data into LLMs for smarter, grounded responses (RAG).
👉 Learn more: https://docs.llamaindex.ai

🔹 4. vLLM
High-performance LLM serving with faster inference and better scaling.
👉 Learn more: https://docs.vllm.ai

🔹 5. Unsloth
Efficient fine-tuning with LoRA & QLoRA — even on limited hardware.
👉 Learn more: https://github.com/unslothai/unsloth

🔹 6. CrewAI
Build multi-agent systems where AI agents collaborate on tasks.
👉 Learn more: https://docs.crewai.com

🔹 7. AutoGPT
Create goal-driven autonomous agents with step-by-step execution.
👉 Learn more: https://github.com/Significant-Gravitas/AutoGPT

🔹 8. LangGraph
Design advanced, stateful workflows with branching logic.
👉 Learn more: https://docs.langchain.com/langgraph

🔹 9. DeepEval
Test and evaluate LLM outputs for accuracy and reliability.
👉 Learn more: https://github.com/confident-ai/deepeval

🔹 10. OpenAI Python SDK
Quickly integrate powerful AI features without managing infrastructure.
👉 Learn more: https://platform.openai.com/docs

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Learn AI for free directly from top companies

1. Anthropic:
anthropic.skilljar.com

2 - Google:
grow.google/ai

3 - Meta:
ai.meta.com/resources/

4 - NVIDIA:
developer.nvidia.com/cuda

5 - Microsoft:
learn.microsoft.com/en-us/training/

6 - OpenAI:
academy.openai.com

7 - IBM:
skillsbuild.org

AWS:
skillbuilder.aws

9 - DeepLearning.AI:
deeplearning.ai

10 - Hugging Face:
huggingface.co/lear

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Best YouTube Channels To Learn AI in 2026

1. Fundamentals – 3Blue1Brown
2. Deep Learning – Andrej Karpathy
3. AI Research – Yannic Kilcher
4. Practical AI – AssemblyAI
5. LLMs – AI Explained
6. ML Theory – StatQuest
7. Papers Simplified – Two Minute Papers
8. GenAI – Matthew Berman
9. AI Agents – Nicholas Renotte
10. Applied ML – Krish Naik
11. PyTorch – Aladdin Persson
12. Math for ML – Serrano Academy
13. Industry Insights – Lex Fridman
14. Real-world AI – DeepLearningAI

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