Top YouTube Channels to Master Tech Skills 🚀

1. SQL 💻
👉 youtube.com/@joeyblue1

2. Excel 📊
👉 youtube.com/@excelisfun

3. Statistics 📈
👉 youtube.com/@statquest

4. Math 🧮
👉 youtube.com/results?search…

5. Python 🐍
👉 youtube.com/@BroCodez

6. Data Analysis 🔍
👉 youtube.com/@AlexTheAnalyst

7. Machine Learning 🤖
👉 youtube.com/@campusx-offic…

8. Deep Learning 🧠
👉 youtube.com/@deeplizard

9. Java ☕
👉 youtube.com/@Telusko

10. Big Data 📦
👉 youtube.com/@thedatatech

11. Data Engineering ⚙️
👉 youtube.com/@dataengineeri…

12. NLP (Natural Language Processing) 🗣️
👉 youtube.com/@codebasics

13. Computer Vision & AI 👁️
👉 youtube.com/@murtazasworks…

14. Generative AI ✨
👉 youtube.com/@sunnysavita10

15. University-Level Courses 🎓
👉 youtube.com/@stanfordonline
👉 youtube.com/@mitocw

16. All-in-One Learning 📚
👉 youtube.com/@freecodecamp

#TechSkills #YouTube #DataScience #Programming #MachineLearning #LearnTech

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Free Generative AI Courses

Generative AI Full Course: Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More
🆓 Free Video Course
⏰ Duration: 30 hrs
🏃‍♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨‍🏫 Instructors: Krish Naik, Sunny Savita & Boktiar Ahmed Bappy via freeCodeCamp
🔗 Course Link

5-Day Gen AI Intensive Course with Google
🆓 Free Video + Hands-On Codelabs
⏰ Duration: 5-day structure
🏃‍♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨‍🏫 Created by: Google & Kaggle
🔗 Course Link

Free GenAI 65-Hour Bootcamp
🆓 Free Video Course
⏰ Duration: 65 hrs
🏃‍♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨‍🏫 Instructor: Andrew Brown (ExamPro) via freeCodeCamp
🔗 Course Link

Generative AI for Beginners
🆓 Free Video Course
⏰ Duration: Multi-hour
🏃‍♂️ Self Paced
📈 Difficulty: Beginner
👨‍🏫 Created by: Great Learning Academy
🔗 Course Link

Introduction to Generative AI
🆓 Free Video Course
⏰ Duration: 45 min
🏃‍♂️ Self Paced
📈 Difficulty: Beginner
👨‍🏫 Created by: Google Skills
🔗 Course Link

Generative AI for Beginners
📚 Text Course
⏰ Duration: 21 lessons
🏃‍♂️ Self Paced
📈 Difficulty: Beginner
👨‍🏫 Created by: Microsoft Cloud Advocates
🔗 Course Link

AI Capabilities and Limitations
🆓 Free Video Course
⏰ Duration: Self-paced
🏃‍♂️ Self Paced
📈 Difficulty: Beginner
👨‍🏫 Created by: Anthropic Academy
🔗 Course Link

Generative AI for Beginners
🆓 Free Video Course
⏰ Duration: 4 hrs
🏃‍♂️ Self Paced
📈 Difficulty: Beginner
👨‍🏫 Created by: Simplilearn
🔗 Course Link



Reading Materials

📖 Prompt Engineering Guide
📖 Awesome Generative AI (Curated Resource List)
📖 Generative AI: A Beginner's Guide
📖 Understanding Generative AI Capabilities
📖Stanford HAI: 2025 AI Index Report

🚀 Stop Maintaining Scrapers. Start Shipping Products.

Build AI products, not scraping infrastructure.

CoreClaw provides ready-to-use Workers & APIs for 1000+ websites — including Google Maps, Instagram, Facebook, YouTube, Amazon, Tiktok and Google Search Scraper.

✔️ No infrastructure
✔️ No proxy management
✔️ No scraper maintenance
✔️ JSON / CSV / REST API

🎁 Create a free account. Get free credits. Explore every Worker.

👉 https://coreclaw.com

Your AI helper right in your messenger — in 5 minutes, free

Amplify (UK) plugs an AI agent straight into your Telegram, WhatsApp, Slack, WeChat, or Discord. Not just a GPT chat — an assistant that reaches into the real world.

Handles it all: emails, reminders, spreadsheets, Telegram-channel digests, image and video generation, PDFs, Google Drive, Notion. Send it voice notes on the go — it gets everything.

Pricing: $10/mo + pay-as-you-go for the AI model, all costs transparent and tracked. Already have OpenAI subscription? Link it and skip paying for the model.

🎁 Promo code CODEPROGRAMMER2 → 2 months free + $10 credit. Bring someone in — another month free.

https://getamplify.team/

🔖 A useful training tool for Data Scientists 📊

🫡 Real-world tasks from IT companies;
🫡 SQL practice;
🫡 Python tasks;
🫡 Preparation for Data Science interviews.

⛓ Link to the training tool
https://www.stratascratch.com/

🏷 #DataScience #SQL #Python #InterviewPrep #TechTraining #DataAnalyst

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

A Powerful Alternative to Pandas 🚀

This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. ⚙️

To get started, simply replace a single import:

import fireducks.pandas as pd

Performance Benchmarks demonstrate speed improvements in various use cases. 📈

More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing

#Pandas #Python #DataScience #Performance #Fireducks #BigData

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Try it, it's free

A collection of resources on MLOps for those who want to understand how machine learning systems are brought to production. 🚀🤖

https://github.com/visenger/awesome-mlops

#MLOps #MachineLearning #DevOps #AI #DataScience #TechResources

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

U-Net by hand ✍️ ~ 17 steps walkthrough below

I consider U-Net as a key milestone in deep learning, the first image-to-image model that really worked!

It came out of medical imaging, an unusual place, not from NeurIPS or CVPR or ACL.

Now it is the backbone of diffusion models, which you see in almost all modern image generation models.

I drew the network as a C so the matrix multiplication flows naturally down.

Tilt your head to the right and it is a U again. 🤣

Goal: push a 3 x 16 image down to a 2 x 4 bottleneck and back out again, filling in every cell yourself.

= 1. Given =

An image of three channels, R, G and B, sixteen pixels wide, and every kernel the network will use.

= 2. Convolution 1 =

Let us slide the first kernel over the image. Each output is one multiply-and-add over a 2 x 3 window, and the result is the green feature map.

= 3. Find the maxima =

We circle the largest value in each 1 x 2 window. Circling first is worth the extra step: it is the pooling decision, made before anything is written down.

= 4. Max pool 1 =

Let us copy those maxima down. Sixteen columns become eight, and half the detail is gone for good.

= 5. Convolution 2 =

We convolve again with the second kernel, deeper into the contracting path. The feature map is blue now.

= 6. Find the maxima again =

Same move as step 3, on the blue map.

= 7. Max pool 2 =

Eight columns become four.

= 8. The bottleneck =

Let us convolve once more. This is the bottom of the U, a 2 x 4 block that is everything the network kept.

= 9. Spread it out =

We start back up. The transposed convolution writes each bottleneck value into a wider grid, leaving gaps between them.

= 10. Transposed convolution 1 =

Let us fill those gaps by convolving over the spread-out grid. Four columns become eight.

= 11. The first skip =

We copy the encoder's matching row straight across. This is the skip connection, and it is the whole reason a U-Net can recover detail that pooling threw away.

= 12. Convolution with the skip =

Let us convolve the upsampled features together with the copied ones.

= 13. Spread it out again =

Same as step 9, one level up.

= 14. Transposed convolution 2 =

Eight columns become sixteen, back to the width we started at.

= 15. The second skip =

The encoder's first feature map comes across, the one made before any pooling happened.

= 16. Convolution and ReLU =

We convolve, then cross out every negative and set it to zero.

= 17. Output convolution =

Let us apply the last kernel. Out comes R', G' and B', an image the same size as the one we started with.

The outputs:

R' = [3, 0, 7, 0, 7, 0, 17, 0, 3, 0, 9, 0, 2, 0, 6, 0]
G' = [1, 20, 1, 10, 1, 12, 1, 19, 2, 5, 1, 11, 1, 3, 1, 7]
B' = [4, 20, 8, 10, 8, 12, 18, 19, 5, 5, 10, 11, 3, 3, 7, 7]

Congrats! You just calculated a U-Net by hand.

💾 Save this post!

#UNet #DeepLearning #AI #NeuralNetworks #ComputerVision #MachineLearning

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🔖 Over 300 real-world case studies of ML systems from top companies. 🤖

We found a repository that collects genuine ML engineering experience – not theory from textbooks, but real stories of implementing models in production. 📚

Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. 🏗️

⛓ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies

#MachineLearning #MLCaseStudies #DataScience #Engineering #Uber #Netflix

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Google just released a free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 🤖⚙️

10% → 17:44 - build your first agent 🚀
30% → 39:30 - Loop engineering: iterate, check, break 🔁
60% → 1:12:38 - Graph engineering 🕸️
75% → 1:34:26 - agents that throttle themselves ⚡
100% → 1:55:05 - full graph for multi-agentic systems 🏗️

everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph.

watch the course, build the graph - then read the full architecture below ↓

More: https://telegra.ph/Graph-Engineering-build-1000-agent-loops-in-one-window-from-one-prompt-full-5-step-course-08-02

#GraphEngineering #AI #Agents #Graphs #Tech #Coding

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Attention Heatmap vs Token Pruning 🔍✂️

🔗 More: https://www.overshoot.ai/blogs/an-introduction-to-token-pruning-for-vlms

#AI #MachineLearning #TokenPruning #DeepLearning #TechNews #VLM

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Generative Adversarial Network (GAN) by hand ✍️ ~ 9 steps walkthrough below

The Gen in GenAI came from this landmark paper by Ian Goodfellow et al., 12 years ago.

The paper showed that a neural network can not only classify but also turn upside down to generate realistic looking images.

The secret? We pit two of them against each other: a Generator turns noise into fake data, and a Discriminator learns to tell fake from real, pushing the Generator to keep doing better.

One runs upside down, the other right way up.

I drew and calculated one entirely by hand.

Goal: generate realistic 4D data out of 2D noise, filling in every cell yourself.

= 1. Given =

Four noise vectors in 2D, and four real data vectors in 4D.

= 2. Generator, first layer =

Let us multiply the noise by weights and biases to get new features.

= 3. ReLU =

We apply the activation, and -1 and -2 are crossed out and set to 0.

= 4. Generator, second layer =

Let us multiply again. ReLU applies here too, but every value is already positive, so nothing changes. What comes out is the fake data F, made by a two-layer generator out of nothing but noise.

= 5. Discriminator, first layer =

We feed it both, the four fakes and the four real vectors, through the same weights. It never learns which is which from the layout, only from the numbers.

= 6. Discriminator, second layer =

Let us reduce each data vector to a single feature Z. Eight vectors in, eight numbers out.

= 7. Sigmoid =

We turn each Z into a probability Y. A 1 means the discriminator is certain the data is real, a 0 means certain it is fake.

= 8. Training the Discriminator =

Let us take the gradients as Y minus YD, where YD is what the discriminator should have said: 0 for the four fakes, 1 for the four real. Why so simple? Because pairing sigmoid with binary cross entropy loss makes the math collapse to exactly this subtraction. Its loss uses both halves of the page.

= 9. Training the Generator =

We do it again, as Y minus YG, and YG is [1, 1, 1, 1]: the generator wants the discriminator to call every fake real. Same predictions, different target, opposite goal. Its loss uses only the fakes.

The outputs:
Fake data F = [1, 2, 3, 1], [1, 1, 2, 1], [2, 2, 4, 2], [1, 0, 1, 1]
Predictions on fakes = [.7, .5, .9, .3]
Predictions on real = [.7, .9, .9, 1]
Discriminator gradients = [.7, .5, .9, .3] and [-.3, -.1, -.1, 0]
Generator gradients = [-.3, -.5, -.1, -.7]

The takeaway: the adversarial part is one subtraction done twice. The same eight predictions, scored against two opposite targets, send one set of gradients back through the blue weights and another back through the green ones.

Python And Flask Framework Complete Course

Python-Powered Proficiency: Depth Introduction To Python Programming And Python Web Framework Flask.…

🏷 Category: it-and-software
🌍 Language: English (US)
👥 Students: 292,248 students
⭐️ Rating: 4.5/5.0 (2,187 reviews)
🏃‍♂️ Enrollments Left: N/A
⏳ Expires In: N/A
💰 Price: $34.79 ⟹ FREE
🆔 Coupon: 25BBPMXNVD35

⚡ Opens instantly — your free link unlocks on its own in seconds, no ad required.

💎 By: https://t.iss.one/Udemy26
#Python #DataScience #Automation #FreeCourse #Udemy #OnlineLearning

🔖 5 Free Courses on AI Agents

1. https://huggingface.co/learn/agents-course — AI Agents Course 🤗

2. https://deeplearning.ai/courses/ai-agents-in-langgraph — AI Agents in LangGraph 🧠

3. https://deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/ — Multi AI Agent Systems with CrewAI 🤖

4. https://microsoft.github.io/AI-For-Beginners/agentic-ai/ — AI Agents for Beginners 🚀

5. https://deeplearning.ai/courses/building-code-agents-with-hugging-face-smolagents — Building Code Agents with Hugging Face smolagents 💻

If you want to learn about Agentic AI, save this collection. 💾

#AI #ArtificialIntelligence #MachineLearning #TechNews #FreeCourses #LearnAI

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A