Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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Forwarded from Data Analytics
If you want to get GOAT-level at software engineering,

save & star these 10 GitHub repos:

1 System design academy

↳ https://github.com/systemdesign42/system-design-academy

2 Public APIs

↳ https://github.com/public-apis/public-apis

3 Tech interview handbook

↳ https://github.com/yangshun/tech-interview-handbook

4 Coding interview university

↳ https://github.com/jwasham/coding-interview-university

5 Engineering leadership

↳ https://github.com/gregorojstersek/resources-to-become-a-great-engineering-leader

6 Freecodecamp

↳ https://github.com/freeCodeCamp/freeCodeCamp

7 Developer roadmaps

↳ https://github.com/kamranahmedse/developer-roadmap

8 Path to senior engineer handbook

↳ https://github.com/jordan-cutler/path-to-senior-engineer-handbook

9 Free programming books

↳ https://github.com/EbookFoundation/free-programming-books

10 Build your own x

↳ https://github.com/codecrafters-io/build-your-own-x

Anything else you'd add?

♻ Repost to help others become better engineers.
https://t.me/DataAnalyticsX 🧑🏻‍💻
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Good evening ✨
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⭐️ If you want to get dangerously good at generative AI,

then save these 10 GitHub repos:

1️⃣ Generative AI for Beginners

→ https://github.com/microsoft/generative-ai-for-beginners

2️⃣ LLM Course

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

3️⃣ Build a Large Language Model From Scratch

→ https://github.com/rasbt/LLMs-from-scratch

4️⃣ Prompt Engineering Guide

→ https://github.com/dair-ai/Prompt-Engineering-Guide

5️⃣ Awesome LLM Apps

→ https://github.com/Shubhamsaboo/awesome-llm-apps

6️⃣ OpenAI Cookbook

→ https://github.com/openai/openai-cookbook

7️⃣ Hands-On Large Language Models

→ https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

8️⃣ GenAI Agents

→ https://github.com/NirDiamant/GenAI_Agents

9️⃣ Awesome Generative AI Guide

→ https://github.com/aishwaryanr/awesome-generative-ai-guide

🔟 Anthropic Courses

→ https://github.com/anthropics/courses

💬 Anything else you'd add to this list?

===

👋 PS - Want my AI Engineering Playbook for FREE?

Get a copy right now:

→ https://systemdesign42.github.io/ai-engineering-playbook/

#GenerativeAI #GitHub #AI #LLM #MachineLearning #TechTips

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🔥 Free IT & Dev Study Kits – 2026 Prep, No Signup

If you’re prepping for cloud, dev, or AI roles this year, these free kits cover the gaps most people skip — cert basics, hands-on labs, and mock exams. No signup traps, no hidden fees.

📘 Cert E-Book — covers Cisco & AWS fundamentals, good for first-time cert takers
→ https://bit.ly/4yrk1Ti

🪜 Online Free Course — structured path, works as a refresher before exams
→ https://bit.ly/4hUprRP

☁️ Free AI Materials — LLM basics to fine-tuning practice notebooks
→ https://bit.ly/4yt1iXy

📊 Cloud Study Guide — cheat-sheet style, handy for interview prep
→ https://bit.ly/4el4Eod

🧠 Free Mock Exam — test yourself before booking the real thing
→ https://bit.ly/4sbWlkl

📌 Save this or forward it to your dev/study group — good resources are worth sharing. 💪

🌐 Community: https://chat.whatsapp.com/CS7VOvQCLZG5BJPazBGm1v
📲 Personalized help:
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#Python #Cloud #AWS #Cisco #AI #MachineLearning #DevOps #ITCertification #FreeResources #TechCareers
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💾 Free Deep Learning Course from NASA

We found a practical course on applying modern AI methods in astronomy and astrophysics.

It covers neural network architectures, generative models, training on scientific data, simulation-based inference, and research AI agents.

⛓ Link to the course
https://deeplearning4astro.com/

#AI #DeepLearning #NASA #Astronomy #MachineLearning #DataScience

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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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🔖 637 interactive modules on ML and AI

On VizLearn, you can visually explore:
🫡 Gradient descent;
🫡 SVM and PCA;
🫡 Bayes' theorem;
🫡 BPE tokenization;
🫡 Query, Key, and Value;
🫡 KV cache;
🫡 Quantization;
🫡 Neural networks and LLMs.

Change the input data and immediately see the results of the calculations.

⛓️ Link to the tutorial
https://enghusseinsheikho.substack.com/p/637-interactive-modules-on-ml-and?r=3hwnr8&utm_campaign=post&utm_medium=web
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Forwarded from Data Analytics
Agentic AI Roadmap 2026

https://t.me/DataAnalyticsX
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The 25 Best Free Resources for Learning AI in 2026

Learning artificial intelligence in 2026 is both easier and harder than it was a couple of years ago. Easier, because the best courses from Stanford and MIT, books by leading researchers, and hands-on lessons from Hugging Face are all openly available. Harder, because there is now far too much material, and the models and tools change every week. We have collected 25 proven free resources and arranged them by stage: from mathematics to LLMs, agents, and reading the latest papers.

Read: https://enghusseinsheikho.substack.com/p/the-25-best-free-resources-for-learning?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
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Forwarded from Machine Learning
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🔖 Interactive CNN Layer-by-Layer Explanation

With the CNN Explainer, you can learn about:
🫡 Input RGB channels;
🫡 Convolution;
🫡 ReLU;
🫡 Max Pooling;
🫡 Activation maps;
🫡 Flatten;
🫡 Softmax;
🫡 Final prediction.

You can upload your own image and see how the network processes it step by step.
⛓ https://poloclub.github.io/cnn-explainer/
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