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😰 15-Hour "Python for Data Science" Course
⚡️ Learn Python for Data Science
https://enghusseinsheikho.substack.com/p/15-hour-python-for-data-science-course?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
#Python #DataScience #Coding #Tech #Learning #FreeCourse
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⚡️ Learn Python for Data Science
https://enghusseinsheikho.substack.com/p/15-hour-python-for-data-science-course?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
#Python #DataScience #Coding #Tech #Learning #FreeCourse
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then save these 10 GitHub repos:
→ https://github.com/microsoft/generative-ai-for-beginners
→ https://github.com/mlabonne/llm-course
→ https://github.com/rasbt/LLMs-from-scratch
→ https://github.com/dair-ai/Prompt-Engineering-Guide
→ https://github.com/Shubhamsaboo/awesome-llm-apps
→ https://github.com/openai/openai-cookbook
→ https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
→ https://github.com/NirDiamant/GenAI_Agents
→ https://github.com/aishwaryanr/awesome-generative-ai-guide
→ https://github.com/anthropics/courses
===
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: https://wa.link/xd2fu6
#Python #Cloud #AWS #Cisco #AI #MachineLearning #DevOps #ITCertification #FreeResources #TechCareers
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
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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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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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🔖 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
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
❤10
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
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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With the CNN Explainer, you can learn about:
You can upload your own image and see how the network processes it step by step.
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🗂 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
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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
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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
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
❤6👍1
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
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
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