Machine Learning with Python

NumPy looks like a calculator and behaves like a memory model. 🧮 Most tutorials start with functions. This one starts with the five decisions NumPy made before you opened it: an array is one block of memory, shape is a contract between arrays, a slice is a view you can write through, the loop belongs in C, and the axis tells you which direction collapses. 15 chapters, 25 runnable examples, and every output copied from a real execution on NumPy 2.0.2. 🚀 Arrays, shapes and the machinery under the math: https://enghusseinsheikho.substack.com/p/numpy-the-mental-model?r=3hwnr8&utmcampaign=post&utmmedium=web&showWelcomeOnShare=true https://t.me/CodeProgrammer ⭐️

pandas — The Mental Model. A practical, example-driven guide in 18 chapters: how tables and indexes actually work, selection and filtering, groupby, reshaping, joins, time series, text, chaining, performance, and what changed in pandas 3.0. Every example is executed, with its real output shown, and the book closes with a one-page cheat sheet. If the field is short, cut it to: A 31-page practical guide to pandas — 18 chapters, every example executed with real output, from indexes and selection to groupby, performance and pandas 3.0. https://t.me/MachineLearning9

Oxford University has launched a free course on "Deep Learning Theory" for the 2026-2027 academic year. The course focuses on the mathematical foundations of deep neural networks and includes 16 lectures that are freely accessible. The program covers neural network architectures, nonlinear transformations, convolutional structures, pooling, optimization methods, dictionary learning, transferability of learned features, adversarial examples, random matrix theory, invariance learning, and other topics. The lectures explore the mathematical theories that explain how deep neural networks learn and what accounts for their unique characteristics. In my opinion, this is particularly valuable material for those who want to understand the mathematics that underlies modern AI systems. https://courses.maths.ox.ac.uk/course/view.php?id=6621 https://t.me/CodeProgrammer

10 Python libraries every AI engineer should know: PyTorch: Deep learning models Hugging Face Transformers: Pretrained AI models LangChain: LLM apps & agents LangGraph: Stateful agent workflows OpenAI Python SDK: OpenAI model integrations Pydantic: Data validation Sentence Transformers: Text embeddings FAISS: Vector similarity search FastAPI: Web APIs MLflow: Experiment tracking & ML lifecycle management Anything else you'd add to this list? https://t.me/CodeProgrammer

🗂 500+ Cheat Sheets on Machine Learning This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning. Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them. ⛓ You can find it here https://github.com/gurovic/MLCheatSheets #MachineLearning #DeepLearning #Python #DataScience #NeuralNetworks #AI ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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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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🔖 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://vizlearn.in/

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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 ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🔥 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: https://wa.link/xd2fu6 #Python #Cloud #AWS #Cisco #AI #MachineLearning #DevOps #ITCertification #FreeResources #TechCareers

⭐️ 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 🌟 Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp

Agentic AI is the future. If you want to become god-level with AI agents, save these 10 GitHub repos: 1 Awesome LLM Apps 🔗 https://github.com/Shubhamsaboo/awesome-llm-apps 2 Generative AI for Beginners 🔗 https://github.com/microsoft/generative-ai-for-beginners 3 LLM Course 🔗 https://github.com/mlabonne/llm-course 4 Prompt Engineering Guide 🔗 https://github.com/dair-ai/Prompt-Engineering-Guide 5 AI Agents for Beginners 🔗 https://github.com/microsoft/ai-agents-for-beginners 6 AI Agents Projects 🔗 https://github.com/ashishpatel26/500-AI-Agents-Projects 7 Hugging Face Agents Course 🔗 https://github.com/huggingface/agents-course 8 Awesome AI Agents 🔗 https://github.com/e2b-dev/awesome-ai-agents 9 GenAI Agents 🔗 https://github.com/NirDiamant/GenAI_Agents 10 Agents Towards Production 🔗 https://github.com/NirDiamant/agents-towards-production Anything else you'd add?

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🔖 A Comprehensive Resource for Machine Learning Practice The platform offers: 🫡 1200+ machine learning tasks; 🫡 Mathematics, from linear algebra to PCA; 🫡 Deep Learning, NLP, and Computer Vision; 🫡 Labs with real datasets; 🫡 Learning paths and interview preparation; 🫡 Projects using GPT, RL, and CUDA. Almost everything can be accessed directly in your browser, and basic tasks are available for free. ⛓️ Link to the platform https://enghusseinsheikho.substack.com/p/a-comprehensive-resource-for-machine?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true #MachineLearning #DeepLearning #DataScience #AI #Coding #Tech ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🔖 A Comprehensive Resource for Machine Learning Practice The platform offers: 🫡 1200+ machine learning tasks; 🫡 Mathematics, from linear algebra to PCA; 🫡 Deep Learning, NLP, and Computer Vision; 🫡 Labs with real datasets; 🫡 Learning paths and interview preparation; 🫡 Projects using GPT, RL, and CUDA. Almost everything can be accessed directly in your browser, and basic tasks are available for free. ⛓ Link to the platform https://www.deep-ml.com/ #MachineLearning #DeepLearning #DataScience #AI #Coding #Tech ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

📚 Download 10 Python books for FREE: 1. Think Python — O’Reilly https://t.co/X7y3pX68IW 2. The Big Book of Small Python Projects https://inventwithpython.com/bigbookpython/ 3. Data Structures https://opendatastructures.org/ods-python.pdf 4. Data Science Handbook https://t.co/9aOLdiAbim 5. Data Analysis https://wesmckinney.com/book/ 6. Data Science https://t.co/TusyBWGfrx 7. Machine Learning https://t.co/7veOwESe8q 8. Statistics https://t.co/waWDQylQpY 9. Think Bayes https://allendowney.github.io/ThinkBayes2/ 10. Clean Code in Python — Packt https://t.co/OHhVgF1L65 #Python #FreeBooks #Programming #DataScience #MachineLearning #Coding ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

One of the key moments when I truly understood how transformers work: 🧠✨ "Stop thinking of a transformer as a conveyor belt, where each layer transforms the output of the previous one." "Start thinking of it as a residual flow." 🌊 Each block in a transformer has a residual connection that adds the input of the block to its output: x' = f(x) + x Because of this addition, the attention mechanism or MLP within the layer – the function f in the formula above – actually does NOT transform the input. Instead, it calculates the information that needs to be ADDED to the input before passing it to the next block! ➕ Imagine the main part of the transformer as a shared whiteboard. 📝 Each block reads what it needs from it and adds its own notes. All changes are additions. Furthermore, layers can exchange information over distances. A block in the first layer can write information, and a block in the fifth layer can read it, even though there is no direct connection between them. 🔗 I owe these ideas to an older article by Anthropic about the architecture of transformers: transformer-circuits.pub/2021/framework #Transformers #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech ✨ Join Best TG