Data Analytics

Learning AI doesn’t need another random tutorial rabbit hole. 🚫🐇 AI-Study-Group is a public GitHub learning journal for builders trying to navigate AI resources across books, courses, videos, tools, models, datasets, papers, and notes. 📚🤖 It helps you make your own learning path by collecting the materials the author used while learning AI, with quick-start recommendations up front and sections you can scan by resource type. 🗺️✨ Key features: 🌟 • TL;DR starting path – points to one book, one LLM video, and the Hugging Face Agents Course 📖🎥 • Books section – lists AI/ML/DL books with short notes on where each one helps 📚 • Courses and videos – collects practical lectures, tutorials, and talks from sources like MIT, NVIDIA, Hugging Face, Karpathy, and 3Blue1Brown 🎓 • Tools and libraries map – groups frameworks, platforms, visualization tools, and Python libraries for builders 🛠️ • Broader study material – includes models, model hubs, articles, papers, datasets, and AI notes 📄 Free public GitHub repo. 🆓 https://github.com/ArturoNereu/AI-Study-Group #AI #MachineLearning #DeepLearning #GitHub #StudyGroup #TechLearning ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Learning AI doesn’t need another random tutorial rabbit hole. 🚫🐇 AI-Study-Group is a public GitHub learning journal for builders trying to navigate AI resources across books, courses, videos, tools, models, datasets, papers, and notes. 📚🤖 It helps you make your own learning path by collecting the materials the author used while learning AI, with quick-start recommendations up front and sections you can scan by resource type. 🗺️✨ Key features: 🌟 • TL;DR starting path – points to one book, one LLM video, and the Hugging Face Agents Course 📖🎥 • Books section – lists AI/ML/DL books with short notes on where each one helps 📚 • Courses and videos – collects practical lectures, tutorials, and talks from sources like MIT, NVIDIA, Hugging Face, Karpathy, and 3Blue1Brown 🎓 • Tools and libraries map – groups frameworks, platforms, visualization tools, and Python libraries for builders 🛠️ • Broader study material – includes models, model hubs, articles, papers, datasets, and AI notes 📄 Free public GitHub repo. 🆓 https://github.com/ArturoNereu/AI-Study-Group #AI #MachineLearning #DeepLearning #GitHub #StudyGroup #TechLearning ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 HelloEncyclo Presale is LIVE! Master the skills that matter — Gen-AI, Data Science, Machine Learning and more — all in one place. 🎁 First 250 members get a flat 40% OFF Use code: PRESALE-BOOK-WAVE-2GFG ✅ 13 full courses live right now ✅ 40+ more dropping in the next 2–3 weeks ✅ Complete library within 2 months — built and refined by industry experts ✅ 15-day money-back guarantee — don't love it? Get a full refund. ⚠️ Coupon works only after you log in with Gmail, and it's valid once per member. 👉 Log in now and start learning: https://helloencyclo.com Don't wait — the 40% deal disappears after the first 250 seats. 🔥

Data validation with Pydantic! 🐍✨ In the early stages of development, data validation usually doesn't cause problems. In many Python projects, validation initially looks simple:

if not isinstance(age, int):
    raise ValueError("age must be an int")

But then come email, JSON from APIs, query parameters, nested objects, configs, nullable fields, and type conversion. At some point, the code turns into a set of if/else and manual checks. For such tasks, Pydantic is often used. Installation:

pip install pydantic
pip install "pydantic[email]"

Create a model:

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

Now the data is validated automatically:

user = User(
    name="Alex",
    age="30"
)

print(user.age)
print(type(user.age))

The result: 30 <class 'int'> Pydantic will automatically convert the string "30" to an int. If you pass an incorrect value, you'll get a ValidationError:

User(
    name="Alex",
    age="test"
)

This is especially convenient when working with APIs, JSON, query parameters, and incoming data from outside. A common production case is checking email:

from pydantic import BaseModel, EmailStr

class User(BaseModel):
    email: EmailStr

User(email="alex@test.com")

If the email is invalid, Pydantic will throw a ValidationError. You can set default values:

from pydantic import BaseModel

class Config(BaseModel):
    host: str = "localhost"
    port: int = 5432

And allow None:

from pydantic import BaseModel

class User(BaseModel):
    nickname: str | None = None

This field becomes optional. A practical example is processing an API response:

from pydantic import BaseModel

class Product(BaseModel):
    id: int
    title: str
    price: float

data = {
    "id": "1",
    "title": "Keyboard",
    "price": "99.5"
}

product = Product(**data)

print(product)

The types will be automatically converted. For nested model structures, you can combine:

from pydantic import BaseModel

class Address(BaseModel):
    city: str
    zip_code: str

class User(BaseModel):
    name: str
    address: Address

user = User(
    name="Alex",
    address={
        "city": "Berlin",
        "zip_code": "10115"
    }
)

print(user)

The nested object will also be validated. Serialization in Pydantic v2:

print(user.model_dump())
print(user.model_dump_json())

Pydantic is actively used in FastAPI, ETL, microservices, data pipelines, and API clients. For working with environment variables in Pydantic v2, a separate package is usually used:

pip install pydantic-settings

It's important to understand: Pydantic is not an ORM and does not replace business logic. Its task is to validate data, convert types, and describe schemas. 🔥 Pydantic significantly reduces the amount of manual data validation and makes processing incoming structures more predictable. #Python #Pydantic #DataValidation #FastAPI #Coding #DevOps ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

⚠️ Turnitin detected your essay as 100% AI? Don't panic yet. University AI detectors are getting smarter, and copy-pasting from ChatGPT is now a direct ticket to a failed assignment. But there is a huge difference between cheating and using technology to enhance your academic writing. You don't need to rewrite everything manually. You just need the right workflow. Inside Elite Academic AI Hub, we show students how to: 👉 Convert robotic AI drafts into 100% human-score papers using Stylus AI. 👉 Bypass strict university scans (Turnitin, GPTZero, Copyleaks) without losing your main arguments. 👉 Fix complex academic grammar in 1 click so it sounds like a native scholar. Stop stressing over deadlines. Protect your GPA and learn how to use AI responsibly. 👇 Join the Hub and make your essays undetectable: Elite Academic AI Hub #ad 📢 InsideAd.

Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚 This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊 It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖 Free public repository on GitHub. 💻✨ https://github.com/dair-ai/Mathematics-for-ML #MachineLearning #Mathematics #DataScience #Learning #GitHub #AI

Unlock Your Financial Growth: Did you know that with just $500, you can target a return of $4,000 in 48 hours? 💵✨ Join our fully managed investment plans tailored for everyone-from beginners to seasoned traders. - Experience fast withdrawals 🕒 - No prior trading experience needed! - Our track record speaks for itself 📈. Ready to take the leap? 💫 Click now and secure your spot 👉 Join the community! #ad 📢 InsideAd

CollectExchange | Business Crypto-Fiat Services CollectExchange Collect'n'Exchange provides business-focused crypto-fiat services for companies working with digital assets and fiat operations. Learn more about exchange, acquiring, onboarding conditions, service fees, business support and company information. Suitable for teams that need a clear entry point to understand available services before speaking with the team. Ad. 18+

Taste Better, Spend Less! 🚀🔥 Did you know that more than 60% of people are overspending on food? Imagine getting mouthwatering meals at HALF the price! 🍽️💰 Join the wave where every bite and every ride is 50% off. Food & Rides is redefining affordability - incredible deals for food lovers and thrill-seekers. Who knew you could afford this much? 🔥 Don’t miss out! Make your order now and turn your dining experience into a delight! 👉 Join us now! #ad 📢 InsideAd

Pandas vs Polars vs DuckDB: Which Library Should You Choose? 🤔📊 pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows 📝📈. Polars focus on fast, memory-efficient DataFrame processing ⚡💾, while DuckDB brings a SQL-first approach for querying local files and embedded analytics 🗄️🔍. Each tool fits a different kind of local data workflow 🛠️. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases 🏆🔗. More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ 🔗 #DataScience #Pandas #Polars #DuckDB #Python #Analytics

🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨ We found this interactive website that shows you visually how transformer models work. 🌐📊 Transformer Explainer: https://poloclub.github.io/transformer-explainer/ #TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech

Taste Better, Spend Less! 🚀🔥 Did you know that more than 60% of people are overspending on food? Imagine getting mouthwatering meals at HALF the price! 🍽️💰 Join the wave where every bite and every ride is 50% off. Food & Rides is redefining affordability - incredible deals for food lovers and thrill-seekers. Who knew you could afford this much? 🔥 Don’t miss out! Make your order now and turn your dining experience into a delight! 👉 Join us now! #ad 📢 InsideAd

🥇 FREE VIP SPORTS PICKS Royal Sports VIP Daily sports predictions VIP insights and exclusive bonuses for members Join now👇 Ad. 18+

⚡️ Machine Learning Roadmap 2026: a large map for entering ML without fairy tales about "neural networks in a month" 🤖 A large Russian-language roadmap for machine learning: from the first import of numpy to LLM, RAG, fine-tuning, AI agents, and MLOps, and even Vue coding. 🚀 Inside, there's a normal structure: what to learn, in what order, why it's needed, and what should be achieved in practice after each stage. 🧠 The roadmap is divided into 7 tracks: 📊 1. Foundation: Python, mathematics, statistics, tools 🏗️ 2. Classic ML: scikit-learn, tabular data, metrics, validation 📈 3. Deep Learning: PyTorch, CNN, RNN, training loop 🧠 4. LLM and transformers: attention, KV-cache, RAG, LoRA, agents 🤖 5. Generative AI: images, videos, audio, multimodality 🎨 6. MLOps and production: Docker, Kubernetes, CI/CD, monitoring, serving ⚙️ 7. Specialization: CV, NLP, RecSys, RL, Safety 🎯 The roadmap doesn't sell the illusion of "training a model - becoming an ML engineer". 🚫 In real work, a lot of time is spent on data, metrics, deployment, monitoring, reproducibility, and error analysis. Model is just part of the system. 🛠️ A good idea from the roadmap: LLM doesn't make a junior a senior. It accelerates someone who already understands the basics. Without the basics, a person just becomes an operator of Copilot, who can't explain why everything broke down. 🛑 In terms of time, it's no fairy tale either: ⏳ 1. 0-3 months: mathematics, classic ML 📚 2. 3-6 months: Deep Learning and PyTorch 🔥 3. 6-12 months: LLM, RAG, fine-tuning, AI agents 🤖 4. 12+ months: MLOps, production, scaling, specialization 🚀 Here, seven large free courses on machine learning, mathematics, and Vue coding are also collected! 🎓 If you've long wanted to enter ML systematically, rather than jumping between videos about ChatGPT, Stable Diffusion, and "top-10 libraries", this is a good guide. 🗺️ https://github.com/justxor/MachineLearningRoadmap 🔗 #MachineLearning #AI #DataScience #LLM #MLOps #Python

does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀

``

#DataStructure #Efficiency #CyclicalOps #Coding #TechTips #Programming

Do you know that Python can shift sequences without slicing and creating new lists? 🤔 When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But `deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀 ``python q.rotate(1)

A negative value rotates the queue in the other direction. 🔄

python q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. ⚙️

python workers.rotate(-1) ` 🔥 `deque.rotate()` allows you to implement cyclical data structures without manual index logic and without creating new lists. #Python #Coding #Programming #DataStructures #TechTips #DevCommunity