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Data Analytics
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho
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| التاريخ | نمو المشتركين | الإشارات | القنوات | |
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منشورات القناة
Repost from Machine Learning with Python
A new collection of free courses has been added:
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Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. 📚
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. 🧠
What's inside:
• Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
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• WIP markers for incomplete materials
• Instructions for contributors on adding and improving notes
The idea was appreciated. 👍
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. 🗺️
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| 5 | The ultimate guide to fine tuning.pdf | 1 975 |
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| 13 | 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 📄
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| 14 | 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 📄
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| 18 | 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.
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| 20 | 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 | 758 |
