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 Machine Learning
Classical machine learning equations and diagrams cheat sheet πŸ“Š

https://github.com/soulmachine/machine-learning-cheat-sheet

#MachineLearning #ML #DataScience #CheatSheet #AI #DeepLearning

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My favorite way to work with multiple filters in pandas.Series β€” not a chain of .loc, but a single mask. 🐼

The chain looks neat, but breaks on real data and easily gives unexpected results:

s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]

The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🀯

It's more reliable to gather everything into one expression:

s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]

One mask, one point of truth. βœ…

It's easier to debug. Fewer surprises when the code grows. πŸš€

#Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging
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Guide to scalable system design and interviews πŸš€πŸ“Š

https://github.com/karanpratapsingh/system-design

#SystemDesign #Scalability #TechInterviews #SoftwareEngineering #DevCommunity #Coding

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πŸš€ Level up your AI & Data Science skills with HelloEncyclo β€” a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
βœ… 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
πŸ”‘ Use code: PRESALE-BOOK-WAVE-2GFG
πŸ‘‰ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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Curated list of distributed systems papers and books πŸ“šπŸ“–

https://github.com/theanalyst/awesome-distributed-systems

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πŸš€ Level up your AI & Data Science skills with HelloEncyclo β€” a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
βœ… 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
πŸ”‘ Use code: PRESALE-BOOK-WAVE-2GFG
πŸ‘‰ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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Anthropic, together with Frontend Masters, have launched a free course on Claude Code. πŸš€

And this is not a superficial overview, but a thorough analysis of the tool for those who want to really improve their vibe coding and work with AI agents. πŸ€–

The course is led by Lydia Hallie from Anthropic. πŸ‘©β€πŸ’»

Inside:

β€’ basics of Claude Code
β€’ skills
β€’ hooks
β€’ sub-agents
β€’ MCP
β€’ plugins
β€’ Agent SDK
β€’ advanced work scenarios

A good entry point for those who want not just to "ask AI to write code", but to build a proper workflow around Claude Code. πŸ› οΈ

Link:
http://frontendmasters.com/courses/claude-code

#Anthropic #FrontendMasters #ClaudeCode #AIAgents #Coding #LLM
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Forwarded from Machine Learning
500 AI/ML/Computer Vision/NLP projects with code πŸš€

This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP 🧠

All examples come with code, so you can not just read them, but immediately analyze and run them βš™οΈ

➑️ Link to GitHub:
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

#AI #MachineLearning #DeepLearning #ComputerVision #NLP #DataScience

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Two good places to start πŸ‘‡

β†’ 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

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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.
❀5πŸ‘1
Machine Learning with Python pinned Β«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.…»
A guide to Loop Engineering has been released β€” a new approach to working with AI agents

The repository loop-engineering has been published, offering a paradigm shift: instead of manually prompting AI agents, the developer designs a cycle that does this automatically. πŸ”„πŸ€–

The author notes that most people still use Claude Code, Codex, Cursor, and Grok as a regular chat: prompt β†’ wait β†’ copy β†’ correct β†’ prompt again. Loop Engineering proposes to stop being a "nanny" for the agent and instead build a system where agents work, check, correct, and escalate on their own. πŸ› οΈβš™οΈ

The repository includes ready-made cycles for daily triage, PR, CI, dependencies, changelog, and issues. It includes CLI for creating cycles, evaluating tokens, auditing the repository, and safely running agents via GitHub Actions. πŸ“‹βœ…

"Prompt engineering was about how to write better prompts. Loop engineering is about creating a system where agents continue to work without your supervision at every step," the description says. πŸš€πŸ§ 

The repository is available on GitHub.

Repository: https://github.com/cobusgreyling/loop-engineering

#LoopEngineering #AI #Agents #GitHub #DevOps #Automation

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