⚡️ 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
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
GitHub
GitHub - justxor/MachineLearningRoadmap: Полный Roadmap по машинному обучению 2026
Полный Roadmap по машинному обучению 2026 . Contribute to justxor/MachineLearningRoadmap development by creating an account on GitHub.
❤3
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
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
❤3
Forwarded from Learn Python Coding
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:
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:
Create a model:
Now the data is validated automatically:
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:
This is especially convenient when working with APIs, JSON, query parameters, and incoming data from outside.
A common production case is checking email:
If the email is invalid, Pydantic will throw a ValidationError. You can set default values:
And allow None:
This field becomes optional. A practical example is processing an API response:
The types will be automatically converted. For nested model structures, you can combine:
The nested object will also be validated. Serialization in Pydantic v2:
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:
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
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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
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Telegram
AI PYTHON 🌟
You’ve been invited to add the folder “AI PYTHON 🌟”, which includes 15 chats.
❤6
🚀 Create an LLM from Scratch!
I came across a great find from Vizuara — a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. 🧠✨
Most people use ChatGPT.
But only a few actually understand how it works under the hood. ⚙️
This playlist step by step breaks down all the key concepts without overloading with complex explanations.
📚 What you will learn:
→ The architecture of Transformer 🏗️
→ The internal structure of GPT
→ Tokenization and BPE 🧩
→ Attention mechanisms 🔍
→ The process of training an LLM 📈
→ Full implementations in Python 🐍
✅ Suitable for:
• ML engineers
• AI enthusiasts
• Developers entering the GenAI field
• Anyone who is tired of explaining AI as a "black box" 🕵️
If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini — this material is worth watching. 👀
🔗 Link to the playlist:
https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgsLAr8YCgCwhPIJNNtexWu
#LLM #AI #MachineLearning #Python #GenAI #DeepLearning
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
I came across a great find from Vizuara — a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. 🧠✨
Most people use ChatGPT.
But only a few actually understand how it works under the hood. ⚙️
This playlist step by step breaks down all the key concepts without overloading with complex explanations.
📚 What you will learn:
→ The architecture of Transformer 🏗️
→ The internal structure of GPT
→ Tokenization and BPE 🧩
→ Attention mechanisms 🔍
→ The process of training an LLM 📈
→ Full implementations in Python 🐍
✅ Suitable for:
• ML engineers
• AI enthusiasts
• Developers entering the GenAI field
• Anyone who is tired of explaining AI as a "black box" 🕵️
If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini — this material is worth watching. 👀
🔗 Link to the playlist:
https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgsLAr8YCgCwhPIJNNtexWu
#LLM #AI #MachineLearning #Python #GenAI #DeepLearning
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Forwarded from Machine Learning with Python
🎁 SPOTO Mid-Year Sale – Grab Your IT Certification Success Kit!
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✅ Free Resources:
・Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/4alTSfk
・IT Certs E-book: https://bit.ly/49ub0zq
・IT Exams Skill Test: https://bit.ly/4dVPapB
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・Free Cloud Study Guide: https://bit.ly/4u7sdG0
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💬 Want exam help? Chat with an admin now:
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Forwarded from Machine Learning
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A Collection of Machine Learning Libraries for Python 🤖
A large repository containing over 900 libraries and frameworks for machine learning. 📚
All projects are sorted by quality and popularity, which helps you quickly find the best tools for working with AI and ML. ⚙️
Repo: https://github.com/ml-tooling/best-of-ml-python?tab=readme-ov-file#vector-similarity-search-ann
#MachineLearning #Python #AI #DataScience #MLTools #Programming
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A large repository containing over 900 libraries and frameworks for machine learning. 📚
All projects are sorted by quality and popularity, which helps you quickly find the best tools for working with AI and ML. ⚙️
Repo: https://github.com/ml-tooling/best-of-ml-python?tab=readme-ov-file#vector-similarity-search-ann
#MachineLearning #Python #AI #DataScience #MLTools #Programming
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