Forwarded from Machine Learning with Python
10 GitHub repositories that are worth checking out for an AI engineer 🤖
1. Hands-On AI Engineering 🛠️
A collection of AI applications and agent systems with practical use cases of LLM.
👉 https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models 📘
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners 🎓
A free course from Microsoft with 11 lessons on creating AI agents.
👉 https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents 🤖
A large collection of tutorials and implementations of agent systems.
👉 https://github.com/NirDiamant/GenAI_Agents
5. Made With ML 🚀
About the development, deployment, and support of production-ready ML systems.
👉 https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering ⚙️
A practical course on Harness Engineering for AI agents.
👉 https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch 🔬
Autonomous cycles of ML experiments from Andrej Karpathy.
👉 https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems 📚
Notes and materials from Chip Huyen's book.
👉 https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference ⚡
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
👉 https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course 🗺️
A practical course on LLM with a roadmap and Colab notebooks.
👉 https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
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1. Hands-On AI Engineering 🛠️
A collection of AI applications and agent systems with practical use cases of LLM.
👉 https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models 📘
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners 🎓
A free course from Microsoft with 11 lessons on creating AI agents.
👉 https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents 🤖
A large collection of tutorials and implementations of agent systems.
👉 https://github.com/NirDiamant/GenAI_Agents
5. Made With ML 🚀
About the development, deployment, and support of production-ready ML systems.
👉 https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering ⚙️
A practical course on Harness Engineering for AI agents.
👉 https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch 🔬
Autonomous cycles of ML experiments from Andrej Karpathy.
👉 https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems 📚
Notes and materials from Chip Huyen's book.
👉 https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference ⚡
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
👉 https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course 🗺️
A practical course on LLM with a roadmap and Colab notebooks.
👉 https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
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Forwarded from Machine Learning with Python
🎓 A Free AI Course for Beginners by Microsoft
For those just getting into artificial intelligence, Microsoft offers a free course.
It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.
The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.
Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.
➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md
What's your AI level right now?
❤️ — Advanced user
🔥 — Almost zero
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
For those just getting into artificial intelligence, Microsoft offers a free course.
It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.
The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.
Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.
➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md
What's your AI level right now?
❤️ — Advanced user
🔥 — Almost zero
#AICourse #Microsoft #DeepLearning #TensorFlow #PyTorch #MachineLearning
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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🔖 Cheat sheets on Transformers and LLMs from Stanford
We found an excellent set of materials from the Stanford CME-295 course on large language models.
They cover tokenization, the self-attention mechanism, prompting, fine-tuning, LLM-as-a-judge, RAG, AI agents, and reasoning models.
⛓️ Download here
https://github.com/afshinea/stanford-cme-295-transformers-large-language-models
We found an excellent set of materials from the Stanford CME-295 course on large language models.
They cover tokenization, the self-attention mechanism, prompting, fine-tuning, LLM-as-a-judge, RAG, AI agents, and reasoning models.
⛓️ Download here
https://github.com/afshinea/stanford-cme-295-transformers-large-language-models
❤3
Forwarded from Machine Learning with Python
The guide Path to Senior Engineer Handbook has gathered resources for developers who want to advance to the level of Senior Engineer. 🚀
Inside: 📚
More than 50 newsletters on professional growth, system design, leadership, and web development. 📈
A selection of books on communication, technical writing, and building working relationships. 🤝
Selected YouTube channels, podcasts, and professional communities. 🎧
Courses, scientific articles, and educational platforms for a deeper study of topics. 🎓
A good starting point for those who want to improve not only their technical skills, but also their architectural thinking, communication, and leadership competencies. 💡
Link: https://github.com/jordan-cutler/path-to-senior-engineer-handbook?utm_source=opensourceprojects.dev&ref=opensourceprojects.dev
#SeniorEngineer #CareerGrowth #SoftwareEngineering #TechLeadership #SystemDesign #DevCommunity
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
Inside: 📚
More than 50 newsletters on professional growth, system design, leadership, and web development. 📈
A selection of books on communication, technical writing, and building working relationships. 🤝
Selected YouTube channels, podcasts, and professional communities. 🎧
Courses, scientific articles, and educational platforms for a deeper study of topics. 🎓
A good starting point for those who want to improve not only their technical skills, but also their architectural thinking, communication, and leadership competencies. 💡
Link: https://github.com/jordan-cutler/path-to-senior-engineer-handbook?utm_source=opensourceprojects.dev&ref=opensourceprojects.dev
#SeniorEngineer #CareerGrowth #SoftwareEngineering #TechLeadership #SystemDesign #DevCommunity
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GitHub
GitHub - jordan-cutler/path-to-senior-engineer-handbook: All the resources you need to get to Senior Engineer and beyond
All the resources you need to get to Senior Engineer and beyond - jordan-cutler/path-to-senior-engineer-handbook
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Forwarded from Machine Learning with Python
Learn AI for free directly from top companies. 🚀
1 - Anthropic:
anthropic.skilljar.com
2 - Google:
grow.google/ai
3 - Meta:
ai.meta.com/resources/
4 - NVIDIA:
developer.nvidia.com/cuda
5 - Microsoft:
learn.microsoft.com/en-us/training/
6 - OpenAI:
academy.openai.com
7 - IBM:
skillsbuild.org
8 - AWS:
skillbuilder.aws
9 - DeepLearning.AI:
deeplearning.ai
10 - Hugging Face:
huggingface.co/learn
💬 Comment "Learning" if you find this helpful.
🔄 Repost so others can take help.
🔖 Must bookmark for future reference.
#AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll
https://t.iss.one/CodeProgrammer
1 - Anthropic:
anthropic.skilljar.com
2 - Google:
grow.google/ai
3 - Meta:
ai.meta.com/resources/
4 - NVIDIA:
developer.nvidia.com/cuda
5 - Microsoft:
learn.microsoft.com/en-us/training/
6 - OpenAI:
academy.openai.com
7 - IBM:
skillsbuild.org
8 - AWS:
skillbuilder.aws
9 - DeepLearning.AI:
deeplearning.ai
10 - Hugging Face:
huggingface.co/learn
💬 Comment "Learning" if you find this helpful.
🔄 Repost so others can take help.
🔖 Must bookmark for future reference.
#AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll
https://t.iss.one/CodeProgrammer
Grow with Google US
AI Training to Grow Your Career | Google
Learn all about AI & how to supercharge your work or business. We offer AI courses and tools that will help you build essential AI skills.
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Forwarded from Udemy Free Coupons
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🔖 Building our own GPT-like model in PyTorch
We've found an excellent repository for those who want to understand how modern LLMs are built under the hood.
Inside — 10 Jupyter notebooks with step-by-step explanations and implementations of key components of language models.
GitHub: https://github.com/analyticalrohit/llms-from-scratch
#PyTorch #LLM #MachineLearning #AI #DeepLearning #DataScience
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
We've found an excellent repository for those who want to understand how modern LLMs are built under the hood.
Inside — 10 Jupyter notebooks with step-by-step explanations and implementations of key components of language models.
GitHub: https://github.com/analyticalrohit/llms-from-scratch
#PyTorch #LLM #MachineLearning #AI #DeepLearning #DataScience
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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Programming Valley
Learn SQL Basics for Data Science | Programming Valley
Course Overview This hands-on SQL specialization helps you build the foundational skills needed to analyze, transform, and manage data using SQL — the most in-demand language for data professionals.You’ll start from zero and progress through practical exercises…
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There are hundreds of AI channels on YouTube. Here's why we made another one.
Most AI content does one of two things: it stays so surface-level it teaches you nothing, or it goes so deep you need a PhD to follow along.
We built Guidely for everyone in between.
→ We start with absolute beginners in mind
→ Then take you deeper, until the details actually click
→ Every guide is reviewed by experienced AI engineers
→ We don't make more content. We make better content.
Whether you build, design, or market products, our goal is simple: leave you thinking "I've never seen it broken down this well."
Two good places to start 👇
→ AI vs ML vs Deep Learning vs GenAI ... But Done Right!
The terms everyone uses. The distinctions are almost never explained clearly. We fix that: youtu.be/72yyLA2wRWc
→ How to Break into AI Engineering in 2026
A senior applied scientist shares what actually matters: youtu.be/42vE7Ij4kdU
If AI has ever felt overwhelming or noisy, this channel is for you. If the content resonates with you, please don’t forget to like and subscribe.
Most AI content does one of two things: it stays so surface-level it teaches you nothing, or it goes so deep you need a PhD to follow along.
We built Guidely for everyone in between.
→ We start with absolute beginners in mind
→ Then take you deeper, until the details actually click
→ Every guide is reviewed by experienced AI engineers
→ We don't make more content. We make better content.
Whether you build, design, or market products, our goal is simple: leave you thinking "I've never seen it broken down this well."
Two good places to start 👇
→ AI vs ML vs Deep Learning vs GenAI ... But Done Right!
The terms everyone uses. The distinctions are almost never explained clearly. We fix that: youtu.be/72yyLA2wRWc
→ How to Break into AI Engineering in 2026
A senior applied scientist shares what actually matters: youtu.be/42vE7Ij4kdU
If AI has ever felt overwhelming or noisy, this channel is for you. If the content resonates with you, please don’t forget to like and subscribe.
YouTube
AI vs ML vs Deep Learning vs GenAI ... But Done Right!
Our blog comparing AI, Machine Learning, Deep Learning, and Generative AI has gained a lot of traction. If you prefer reading, you can check it out here:
https://guidely.tech/blog/ai-vs-machine-learning-vs-deep-learning-vs-genai
But we know not everyone…
https://guidely.tech/blog/ai-vs-machine-learning-vs-deep-learning-vs-genai
But we know not everyone…
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Data Analytics pinned «There are hundreds of AI channels on YouTube. Here's why we made another one. Most AI content does one of two things: it stays so surface-level it teaches you nothing, or it goes so deep you need a PhD to follow along. We built Guidely for everyone in between.…»
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Curates LLM tools and research for scientific discovery 🧬🔬
Repo: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery 🔗🚀
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📊 Cloud Study Guide → https://bit.ly/4y0HyeW
🧠 Free Mock Exam → https://bit.ly/4ff8jos
Tag a friend who's also on this journey – Get certified together! 💪
🌐 Join the community: https://chat.whatsapp.com/FmbIbbqm2QhKglVpVTSH4d/
📲 Need personalized help? → https://wa.link/6k7042
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Data Analytics
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Your second brain needs more than a demo chatbot
second-brain-ai-assistant-course is an open-source repository of a course from Decoding AI for developers who want to create an assistant for a personal knowledge base with LLMs, agents, RAGs, and LLMOps.
It helps transition from scattered notes to a full-fledged assistant through six modules: data pipelines, dataset generation, fine-tuning, deployment, advanced RAGs, and agent inference/observability.
Key features:
• Six modules - covers architecture, ETL, fine-tuning, deployment, RAGs, and LLMOps
• Offline + online applications - separates ML/data pipelines and the assistant's inference pipeline
• Notion support - uses the Second Brain knowledge base, a public snapshot is available, so a Notion account is not required
• Real LLMOps tools - includes ZenML, Opik, Comet, Unsloth, MongoDB, Hugging Face, and OpenAI
• Developer-oriented - module documentation, ready-made code, uv/ruff, and Docker infrastructure
Open source code (MIT license).
https://github.com/decodingai-magazine/second-brain-ai-assistant-course
second-brain-ai-assistant-course is an open-source repository of a course from Decoding AI for developers who want to create an assistant for a personal knowledge base with LLMs, agents, RAGs, and LLMOps.
It helps transition from scattered notes to a full-fledged assistant through six modules: data pipelines, dataset generation, fine-tuning, deployment, advanced RAGs, and agent inference/observability.
Key features:
• Six modules - covers architecture, ETL, fine-tuning, deployment, RAGs, and LLMOps
• Offline + online applications - separates ML/data pipelines and the assistant's inference pipeline
• Notion support - uses the Second Brain knowledge base, a public snapshot is available, so a Notion account is not required
• Real LLMOps tools - includes ZenML, Opik, Comet, Unsloth, MongoDB, Hugging Face, and OpenAI
• Developer-oriented - module documentation, ready-made code, uv/ruff, and Docker infrastructure
Open source code (MIT license).
https://github.com/decodingai-magazine/second-brain-ai-assistant-course
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