🚀 Master Data Science & Programming!

Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!


🔰 Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://news.1rj.ru/str/CodeProgrammer

🔖 Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://news.1rj.ru/str/DataScienceM

🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://news.1rj.ru/str/DataScience4

🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://news.1rj.ru/str/DataScienceQ

💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://news.1rj.ru/str/datasets1

🧑‍🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://news.1rj.ru/str/DataScienceC

😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://news.1rj.ru/str/DataScienceT

💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9

🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/PythonArab

🖊 Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://news.1rj.ru/str/DataScienceN

📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://news.1rj.ru/str/DataScienceV

📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://news.1rj.ru/str/DataAnalyticsX

🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://news.1rj.ru/str/Python53

⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://news.1rj.ru/str/DataScienceY

━━━━━━━━━━━━━━━━━━
Admin: @HusseinSheikho

⚡️ Running DeepSeek on our computer using Python

Do you want an LLM on your computer: to work offline, not leak data, and seamlessly integrate into a bot? Then let's take DeepSeek Coder and get started!

⚙️ Installing dependencies:

pip install -U transformers accelerate torch

▶️ Example code:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "deepseek-ai/deepseek-coder-6.7b-base"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.float16,   # if the GPU supports fp16
    device_map="auto"            # if there's a GPU — it will use it
)
model.eval()

prompt = "Write a Telegram feedback bot on aiogram"

inputs = tokenizer(prompt, return_tensors="pt")
device = next(model.parameters()).device
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=180,
        do_sample=True,      # IMPORTANT: otherwise the temperature doesn't affect
        temperature=0.7,
        top_p=0.9
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

➕ Advantages:
— works locally (after downloading the weights);
— easily integrates into Telegram/Discord/CLI;
— can be accelerated on the GPU via device_map="auto".

If memory is limited — there are quantized versions (4bit/8bit) and GGUF.

👍 Saving

#python #soft #code

Offline text recognition using DeepSeek-OCR AI: https://github.com/th1nhhdk/local_ai_ocr

👉 @DataScienceN

Deploy any ML model as an MCP server.

LitServe: the simplest way to roll out agents, RAG, and generally any ML models. Now with MCP support.

Completely open-source.

👉 @DataScienceN

This repository collects everything you need to use AI and LLM in your projects.

120+ libraries, organized by development stages:

→ Model training, fine-tuning, and evaluation
→ Deploying applications with LLM and RAG
→ Fast and scalable model launch
→ Data extraction, crawlers, and scrapers
→ Creating autonomous LLM agents
→ Prompt optimization and security

Repo: https://github.com/KalyanKS-NLP/llm-engineer-toolkit

🥺 https://news.1rj.ru/str/DataAnalyticsX