100 YouTube Channels That’ll Actually Make You Smarter 🎯
If you’re serious about learning faster (without boring textbooks), this list is gold.
From science and tech to psychology, finance, and creativity these channels cover it all:
🔬 Science & Tech
• Vsauce: https://t.co/jaGGPDPqLT
• SciShow: https://t.co/Vji8deoUPp
• Veritasium: https://t.co/77adutqTNB
• Kurzgesagt: https://t.co/acGPlkDLKV
• AsapSCIENCE: https://t.co/IF4qoryi2H
🧠 Philosophy & Thinking
• https://t.co/AVgxWpJ2AO: https://t.co/oVTfmYyqQH
• Philosophy Tube: https://t.co/zig0ccc78I
• Academy of Ideas: https://t.co/rmAi9STGpH
• The School of Life: https://t.co/8KEhSyHaAx
• Wireless Philosophy: https://t.co/9Q4jryE3lU
⚛️ Physics
• Fermilab: https://t.co/ZpxkN5WkoX
• Physics Girl: https://t.co/zuFTsVwaIW
• MinutePhysics: https://t.co/bootOLh9ZK
• ScienceClic English: https://t.co/4buWEUp1Z0
• Scienceclic Universe: https://t.co/LWpYe4sL7m
💻 Code & Programming
• freeCodeCamp: https://t.co/hqRlHMYlrj
• Traversy Media: https://t.co/5x9vqKMeC6
• Programming with Mosh: https://t.co/k93emwxooa
• The Coding Train: https://t.co/BcUdtubquI
• CS Dojo: https://t.co/DtWrX2mGgT
📈 Economics & Finance
• Two Cents: https://t.co/LMRyFAabOT
• Graham Stephan: https://t.co/73XKK0SCx2
• Economics Explained: https://t.co/x3PiDklGC3
• The Plain Bagel: https://t.co/Kj8pE4aKnv
🧮 Mathematics
• 3Blue1Brown: https://t.co/5gqb2HkMB2
• Khan Academy: https://t.co/H3mXM5wy9L
• PatrickJMT: https://t.co/OujSNmFAvK
• Tibees: https://t.co/e6wo650CBW
🚀 Astronomy & Space
• SEA: https://t.co/kbrhP8nI9r
• PBS Space Time: https://t.co/UzQfloQBkF
• Anton Petrov: https://t.co/zaf7xtHpfW
• Fraser Cain: https://t.co/7iPFVs3szy
🎨 Art & Design
• Proko: https://t.co/LW74qE14gI
• Jazza: https://t.co/EWcKyiTbSv
• Sycra: https://t.co/YQ2cuhYlDU
🧩 Psychology & Personal Growth
• Psych2Go: https://t.co/964ra5EKH1
• Andrew Huberman: https://t.co/wT5Tyui312
• Ali Abdaal: https://t.co/kK6LxdfjES
• Matt D’Avella: https://t.co/fe9ArtLOHU
The internet is full of distractions.
But it’s also one of the greatest universities ever created if you follow the right creators.
📌 Save this you’ll keep coming back to it.
Which YouTube channel has taught you the most? 👇
Follow me @TechByArti for more.
🚨 Anthropic just showed a 27-minute workshop on how to actually do prompts for Claude.
Taught by the people who built it.
Free. No registration. No paywall.
I've seen $300 courses that don't cover what they teach in the first 8 minutes.
Watch it and bookmark it now.
🚨 Anthropic just showed a 27-minute workshop on how to actually do prompts for Claude.
Taught by the people who built it.
Free. No registration. No paywall.
I've seen $300 courses that don't cover what they teach in the first 8 minutes.
Watch it and bookmark it now.
Want an AI Data Scientist job? Stop doing this:
• Chasing 10+ random certificates
• Relearning high school calculus for 6 months
• Watching YouTube tutorials with no real project
Do this instead 👇
• Pick 1 roadmap
• Build 1 end-to-end AI project
• Ship it: put it on the cloud + portfolio
I give you that roadmap here:
👉 https://t.co/QltD34cwYZ
Understanding AI models is the foundation.
IBM's complete playlist. 34 videos. Free.
Most engineers jump straight to LLMs without understanding the basics.
→ Can't explain how CNNs work
→ Don't know when to use transformers vs LSTMs
→ Confused about GANs and autoencoders
→ Lost when models fail in production
This playlist builds the foundation.
➕ What's covered:
➡️ Core AI Architectures
→ CNNs, GANs, Transformers, LSTMs, Autoencoders
→ How each architecture works
→ When to use which model
➡️ Language Models
→ How Large Language Models Work
→ NLP vs NLU vs NLG
→ Zero Shot Reasoning
→ Building RAG systems
➡️ Training & Optimization
→ Backpropagation explained
→ Gradient Descent
→ Overfitting and Underfitting
→ Federated Learning
➡️ ML Fundamentals
→ Supervised vs Unsupervised Learning
→ Random Forest
→ Time Series Analysis
→ Monte Carlo Simulation
➡️ Production AI
→ MLOps
→ PyTorch for training and inference
→ Embedded AI
→ Edge AI vs Distributed AI
➡️ Modern AI
→ AI Agents
→ Large Reasoning Models (LRMs)
→ LLM as a Judge
→ AI Biases and Trust
➕ Real outcomes:
✓ Understand different AI architectures
✓ Know which model to use for your problem
✓ Debug training issues
✓ Deploy models to production
✓ Evaluate AI systems properly
From IBM Technology:
Perfect for:
→ AI engineers building foundations
→ ML engineers understanding architectures
→ Developers learning AI models
→ Anyone deploying AI systems
Build the foundation first. Everything else follows.
(Playlist in comments)
♻️ Repost to save someone $$$ and a lot of confusion.
✔️ Follow @techNmak for more AI/ML insights.
OpenAI is providing this group of professionals with a real advantage at work, not just for their own careers and productivity, but ultimately for enabling them to more efficiently develop the next generation of leaders, tech adopters, and business professionals. https://t.co/pOrv3GrHU1
This in-person vs. hybrid debate is strategic for managing a critical aspect at organizations: talent acquisition and retention. https://t.co/IMsAQ8KWMA
A strong trend shows many modern startups are choosing Golang for their backend.
Here's a curated list of must learn tutorials to help you become the best at backend golang development.
7 small steps to start with algorithmic trading:
1. Start with Python
2. Learn to use VSCode
3. Take a pandas tutorial
4. Then a plotly tutorial
5. Make a portfolio: riskfolio
6. Make a backtest: vectorbt
7. Analyze performance with vectorbt
You can do this! Want to learn how?