Machine Learning
39.4K subscribers
4.35K photos
40 videos
50 files
1.42K links
Real Machine Learning β€” simple, practical, and built on experience.
Learn step by step with clear explanations and working code.

Admin: @HusseinSheikho || @Hussein_Sheikho
Download Telegram
πŸ“Œ Build and Deploy Your First Supply Chain App in 20 Minutes

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2025-12-04 | ⏱️ Read time: 21 min read

A factory operator that discovered happiness by switching from notebook to streamlit – (Image Generated…

#DataScience #AI #Python
πŸ“Œ Bootstrap a Data Lakehouse in an Afternoon

πŸ—‚ Category: DATA ENGINEERING

πŸ•’ Date: 2025-12-04 | ⏱️ Read time: 12 min read

Using Apache Iceberg on AWS with Athena, Glue/Spark and DuckDB

#DataScience #AI #Python
πŸ“Œ The Best Data Scientists are Always Learning

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2025-12-04 | ⏱️ Read time: 7 min read

Why continuous learning matters & how to come up with topics to study

#DataScience #AI #Python
If you want to truly understand how AI systems like #GPT, #Claude, #Llama or #Mistral work at their core, these 85 foundational concepts are essential. The visual below breaks down the most important ideas across the full #AI and #LLM landscape.

https://t.me/CodeProgrammer βœ…
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ“Œ Reading Research Papers in the Age of LLMs

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ Date: 2025-12-06 | ⏱️ Read time: 10 min read

How I keep up with papers with a mix of manual and AI-assisted reading

#DataScience #AI #Python
πŸ€–πŸ§  Supervised Reinforcement Learning: A New Era of Step-Wise Reasoning in AI

πŸ—“οΈ 23 Nov 2025
πŸ“š AI News & Trends

In the evolving landscape of artificial intelligence, large language models (LLMs) like GPT, Claude and Qwen have demonstrated remarkable abilities from generating human-like text to solving complex problems in mathematics, coding, and logic. Yet, despite their success, these models often struggle with multi-step reasoning, especially when each step depends critically on the previous one. Traditional ...

#SupervisedReinforcementLearning #StepWiseReasoning #ArtificialIntelligence #LargeLanguageModels #MultiStepReasoning #AIBreakthrough
❀3
πŸ€–πŸ§  CALM: Revolutionizing Large Language Models with Continuous Autoregressive Learning

πŸ—“οΈ 23 Nov 2025
πŸ“š AI News & Trends

Large Language Models (LLMs) such as GPT, Claude and Gemini have dramatically transformed artificial intelligence. From generating natural text to assisting in code and research, these models rely on one fundamental process: autoregressive generation predicting text one token at a time. However, this sequential nature poses a critical efficiency bottleneck. Generating text token by token ...

#CALM #ContinuousAutoregressiveLearning #LargeLanguageModels #AutoregressiveGeneration #AIEfficiency #AIInnovation
❀1
πŸ€–πŸ§  Agent-o-rama: The End-to-End Platform Transforming LLM Agent Development

πŸ—“οΈ 23 Nov 2025
πŸ“š AI News & Trends

As large language models (LLMs) become more capable, developers are increasingly using them to build intelligent AI agents that can perform reasoning, automation and decision-making tasks. However, building and managing these agents at scale is far from simple. Challenges such as monitoring model behavior, debugging reasoning paths, testing reliability and tracking performance metrics can make ...

#AgentoRama #LLMAgents #EndToEndPlatform #AIIntelligence #ModelMonitoring #AIDevelopment
πŸ€–πŸ§  DeepEyesV2: The Next Leap Toward Agentic Multimodal Intelligence

πŸ—“οΈ 23 Nov 2025
πŸ“š AI News & Trends

The evolution of artificial intelligence has reached a stage where models are no longer limited to understanding text or images independently. The emergence of multimodal AI systems capable of processing and reasoning across multiple types of data has transformed how machines interpret the world. Yet, most existing multimodal models remain passive observers, unable to act ...

#DeepEyesV2 #AgenticMultimodalIntelligence #MultimodalAI #AIEvolution #ActiveReasoning #AIAction
πŸ“Œ The Machine Learning β€œAdvent Calendar” Day 6: Decision Tree Regressor

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2025-12-06 | ⏱️ Read time: 10 min read

During the first days of this Machine Learning Advent Calendar, we explored models based on…

#DataScience #AI #Python
πŸ€–πŸ§  Reducing Hallucinations in Vision-Language Models: A Step Forward with VisAlign

πŸ—“οΈ 24 Nov 2025
πŸ“š AI News & Trends

As artificial intelligence continues to evolve, Large Vision-Language Models (LVLMs) have revolutionized how machines understand and describe the world. These models combine visual perception with natural language understanding to perform tasks such as image captioning, visual question answering and multimodal reasoning. Despite their success, a major problem persists – hallucination. This issue occurs when a ...

#VisAlign #ReducingHallucinations #VisionLanguageModels #LVLMs #MultimodalAI #AISafety
❀1
πŸ€–πŸ§  LEANN: The Bright Future of Lightweight, Private, and Scalable Vector Databases

πŸ—“οΈ 24 Nov 2025
πŸ“š AI News & Trends

In the rapidly expanding world of artificial intelligence, data storage and retrieval efficiency have become major bottlenecks for scalable AI systems. The growth of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) has further intensified the demand for fast, private and space-efficient vector databases. Traditional systems like FAISS or Milvus while powerful, are resource-heavy and ...

#LEANN #LightweightVectorDatabases #PrivateAI #ScalableAI #RAG #AIDataStorage
❀1
πŸ€–πŸ§  Omnilingual ASR: Meta’s Breakthrough in Multilingual Speech Recognition for 1600+ Languages

πŸ—“οΈ 24 Nov 2025
πŸ“š AI News & Trends

In an increasingly connected world, speech technology plays a vital role in bridging communication gaps across languages and cultures. Yet, despite rapid progress in Automatic Speech Recognition (ASR), most commercial systems still cater to only a few dozen major languages. Billions of people who speak lesser-known or low-resource languages remain excluded from the benefits of ...

#OmnilingualASR #MultilingualSpeechRecognition #MetaAI #LowResourceLanguages #SpeechTechnology #GlobalCommunication
πŸ€–πŸ§  Whisper by OpenAI: The Revolution in Multilingual Speech Recognition

πŸ—“οΈ 25 Nov 2025
πŸ“š AI News & Trends

Speech recognition has evolved rapidly over the past decade, transforming the way we interact with technology. From voice assistants to transcription services and real-time translation tools, the ability of machines to understand human speech has redefined accessibility, communication and automation. However, one of the major challenges that persisted for years was achieving robust, multilingual and ...

#Whisper #MultilingualSpeechRecognition #OpenAI #SpeechRecognition #AIAccessibility #VoiceTechnology
❀1
πŸ“Œ How We Are Testing Our Agents in Dev

πŸ—‚ Category: AGENTIC AI

πŸ•’ Date: 2025-12-06 | ⏱️ Read time: 5 min read

Testing that your AI agent is performing as expected is not easy. Here are a…

#DataScience #AI #Python
Generating Fake Data in Python!

Instead of spending time coming up with test data β€” everything can be generated automatically using the Faker library.

Installing the library:
pip install faker


Importing and configuring:
from faker import Faker

# Specify the localization
fake = Faker('ru_RU')


Generating basic data:
print(fake.name())
print(fake.address().replace('\n', ', '))
print(fake.text(max_nb_chars=200))
print(fake.email())
print(fake.country())


After running, you will get random values for the name, address, description, email, and country.

Generating multiple records:
for _ in range(5):
    print({
        "name": fake.name(),
        "email": fake.email(),
        "address": fake.address().replace('\n', ', '),
        "lat": float(fake.latitude()),
        "lon": float(fake.longitude()),
        "website": fake.url()
    })


πŸ”₯ Ideal for test filling of databases. A great way to practice working with external libraries and generating data.

πŸšͺ https://t.me/DataScienceM
Please open Telegram to view this post
VIEW IN TELEGRAM
❀4
πŸ“Œ How to Climb the Hidden Career Ladder of Data Science

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2025-12-07 | ⏱️ Read time: 14 min read

The behaviors that get you promoted

#DataScience #AI #Python
❀1
πŸ“Œ The Machine Learning β€œAdvent Calendar” Day 7: Decision Tree Classifier

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2025-12-07 | ⏱️ Read time: 8 min read

In Day 6, we saw how a Decision Tree Regressor finds its optimal split by…

#DataScience #AI #Python
❀1
I'm pleased to invite you to join my private Signal group.

All my resources will be free and unrestricted there. My goal is to build a clean community exclusively for smart programmers, and I believe Signal is the most suitable platform for this (Signal is the second most popular app after WhatsApp in the US), making it particularly suitable for us as programmers.

https://signal.group/#CjQKIPcpEqLQow53AG7RHjeVk-4sc1TFxyym3r0gQQzV-OPpEhCPw_-kRmJ8LlC13l0WiEfp
❀1