Forwarded from Machine Learning with Python
Learn AI for free directly from top companies. ๐
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
๐ฌ Comment "Learning" if you find this helpful.
๐ Repost so others can take help.
๐ 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
๐ฌ Comment "Learning" if you find this helpful.
๐ Repost so others can take help.
๐ 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.
โค4
A free MIT guide to key computer vision concepts ๐
Link: https://visionbook.mit.edu/ ๐
#ComputerVision #MIT #AI #MachineLearning #Tech #DataScience
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
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Link: https://visionbook.mit.edu/ ๐
#ComputerVision #MIT #AI #MachineLearning #Tech #DataScience
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
โค1
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Multi-agent RL is beautiful precisely at the moment when it starts to converge. ๐คโจ
#MultiAgent #RL #ReinforcementLearning #AI #MachineLearning #DeepLearning
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โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
#MultiAgent #RL #ReinforcementLearning #AI #MachineLearning #DeepLearning
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โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
โค1๐คฉ1
500 AI/ML/Computer Vision/NLP projects with code ๐
This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP ๐ง
All examples come with code, so you can not just read them, but immediately analyze and run them โ๏ธ
โก๏ธ 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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โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP ๐ง
All examples come with code, so you can not just read them, but immediately analyze and run them โ๏ธ
โก๏ธ 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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โค4
Forwarded from Machine Learning with Python
A guide to Loop Engineering has been released โ 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. ๐๐ค
The author notes that most people still use Claude Code, Codex, Cursor, and Grok as a regular chat: prompt โ wait โ copy โ correct โ 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. ๐ ๏ธโ๏ธ
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. ๐โ
"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. ๐๐ง
The repository is available on GitHub.
Repository: https://github.com/cobusgreyling/loop-engineering
#LoopEngineering #AI #Agents #GitHub #DevOps #Automation
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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. ๐๐ค
The author notes that most people still use Claude Code, Codex, Cursor, and Grok as a regular chat: prompt โ wait โ copy โ correct โ 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. ๐ ๏ธโ๏ธ
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. ๐โ
"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. ๐๐ง
The repository is available on GitHub.
Repository: https://github.com/cobusgreyling/loop-engineering
#LoopEngineering #AI #Agents #GitHub #DevOps #Automation
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โค5
A Chinese developer has released an open-source replacement for NumPy that performs calculations on GPUs. It's called CuPy ๐. In many cases, it's enough to replace a single line:
The same code can run on CUDA up to 100 times faster โก๏ธ.
What it can do:
โ Compatible with existing NumPy and SciPy code ๐ ๏ธ.
โ No need to rewrite the program or learn new syntax ๐.
โ Supports not only CUDA but also AMD ROCm ๐ป.
The project is completely open-source ๐:
๐ https://github.com/cupy/cupy
#Python #GPU #NumPy #CuPy #AI #DeepLearning
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import cupy as cp
The same code can run on CUDA up to 100 times faster โก๏ธ.
What it can do:
โ Compatible with existing NumPy and SciPy code ๐ ๏ธ.
โ No need to rewrite the program or learn new syntax ๐.
โ Supports not only CUDA but also AMD ROCm ๐ป.
The project is completely open-source ๐:
๐ https://github.com/cupy/cupy
#Python #GPU #NumPy #CuPy #AI #DeepLearning
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โค6
Don't learn ML by randomly jumping through tutorials. ๐ซ๐
DS-ML Bootcamp is a public repository for a Data Science and machine learning course for beginners who want a structured path from zero to practical projects. ๐๐
It helps transition from installation and concepts to practical ML work, organizing lessons, assignments, code examples, datasets, and solutions around the main machine learning workflow. ๐ ๏ธ๐ง
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment ๐๐
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression ๐๐งฎ
- Practical materials - assignments give learners structured tasks, not just reading notes โ๏ธโ
- Code + datasets - Python examples and raw CSV datasets included for exercises ๐๐
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons ๐ป๐
Free public repository on GitHub. ๐
https://github.com/goobolabs/ds-ml-bootcamp
#MachineLearning #DataScience #Coding #Python #AI #Learning
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DS-ML Bootcamp is a public repository for a Data Science and machine learning course for beginners who want a structured path from zero to practical projects. ๐๐
It helps transition from installation and concepts to practical ML work, organizing lessons, assignments, code examples, datasets, and solutions around the main machine learning workflow. ๐ ๏ธ๐ง
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment ๐๐
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression ๐๐งฎ
- Practical materials - assignments give learners structured tasks, not just reading notes โ๏ธโ
- Code + datasets - Python examples and raw CSV datasets included for exercises ๐๐
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons ๐ป๐
Free public repository on GitHub. ๐
https://github.com/goobolabs/ds-ml-bootcamp
#MachineLearning #DataScience #Coding #Python #AI #Learning
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GitHub
GitHub - goobolabs/Jun-ds-ml-bootcamp: Data Science and Machine Learning Bootcamp. (Jun - 2026)
Data Science and Machine Learning Bootcamp. (Jun - 2026) - goobolabs/Jun-ds-ml-bootcamp
โค7
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๐ฅ Free IT Cert Resources โ Grab Them While They're Hot!
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โค8
Cheat sheet for Scikit-learn: ๐ Scikit-learn is a Python library for machine learning.
๐ฅ Loading Data - downloading and preparing data.
๐งผ Preprocessing - standardization, normalization, and feature processing.
๐๏ธ Create Your Model - creating models for classification, regression, and clustering.
๐ฏ Model Fitting - training the model on data.
๐ฎ Prediction - obtaining forecasts.
๐ Evaluate Performance - assessing the quality of the model using various metrics.
๐ Cross-Validation - checking the model on different samples.
โ๏ธ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.
#ScikitLearn #MachineLearning #Python #DataScience #AI #MLOps
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๐ฅ Loading Data - downloading and preparing data.
๐งผ Preprocessing - standardization, normalization, and feature processing.
๐๏ธ Create Your Model - creating models for classification, regression, and clustering.
๐ฏ Model Fitting - training the model on data.
๐ฎ Prediction - obtaining forecasts.
๐ Evaluate Performance - assessing the quality of the model using various metrics.
๐ Cross-Validation - checking the model on different samples.
โ๏ธ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.
#ScikitLearn #MachineLearning #Python #DataScience #AI #MLOps
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โค6๐2