Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
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Tarmoq:📈 Telegram kanali Machine Learning with Python analitikasi
Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 68 136 obunachidan iborat bo'lib, Taʼlim toifasida 2 365-o'rinni va Hindiston mintaqasida 4 731-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 68 136 obunachiga ega bo‘ldi.
31 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 80 ga, so‘nggi 24 soatda esa 1 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 4.09% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.54% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 784 marta ko‘riladi; birinchi sutkada odatda 1 052 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 5 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 01 Sentabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
68 136
Obunachilar
+124 soatlar
-97 kunlar
+8030 kunlar
Postlar arxiv
LangExtract
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
GitHub: https://github.com/google/langextract
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🚀 Model Context Protocol (MCP) Curriculum for Beginners
Learn MCP with Hands-on Code Examples in C#, Java, JavaScript, Python, and TypeScript
🧠 Overview of the Model Context Protocol Curriculum
The Model Context Protocol (MCP) is an innovative framework designed to standardize communication between AI models and client applications. This open-source curriculum provides a structured learning path, featuring practical coding examples and real-world scenarios across popular programming languages such as C#, Java, JavaScript, TypeScript, and Python.
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● Understand perceptrons, sigmoid, ReLU, tanh
● Learn cost functions, gradient descent, and derivatives
● Implement binary logistic regression using NumPy
𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗦𝗵𝗮𝗹𝗹𝗼𝘄 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗪𝗲𝗲𝗸 𝟯-𝟰)
● Build a neural net with one hidden layer
● Compare activation functions (sigmoid vs tanh vs ReLU)
● Train your model to classify simple images
𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗗𝗲𝗲𝗽 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗪𝗲𝗲𝗸 𝟱-𝟲)
● Forward and backward propagation through multiple layers
● Parameter initialization and tuning
● Implement L-layer neural networks from scratch
𝗣𝗵𝗮𝘀𝗲 𝟰: 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗥𝗲𝗴𝘂𝗹𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗪𝗲𝗲𝗸 𝟳-𝟴)
● Learn mini-batch gradient descent, RMSProp, and Adam
● Apply L2 and Dropout regularization to avoid overfitting
● Boost your model’s performance with better convergence
𝗣𝗵𝗮𝘀𝗲 𝟱: 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄 & 𝗥𝗲𝗮𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗪𝗲𝗲𝗸 𝟵-𝟭𝟬)
● Build models using TensorFlow and Keras
● Normalize data, tune hyperparameters, and visualize metrics
● Create multi-class classifiers using softmax
𝗣𝗵𝗮𝘀𝗲 𝟲: 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 & 𝗖𝗮𝗿𝗲𝗲𝗿 𝗣𝗿𝗲𝗽 (𝗪𝗲𝗲𝗸 𝟭𝟭-𝟭𝟮)
● Work on image recognition, text classification, and real datasets
● Learn model deployment techniques
● Prepare for interviews with hands-on projects and GitHub repo
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✅ Papers:
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🟡 Tree of Thoughts → https://lnkd.in/eiadYm8S
🟡 Reflexion → https://lnkd.in/eggND2rZ
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Top 140 PyTorch Interview Questions and Answers
This comprehensive guide covers essential PyTorch interview questions across multiple categories, with detailed explanations for each.these 140 carefully curated questions represent the most important concepts you'll encounter in #PyTorch interviews.
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800+ Data Science Interview Questions – A Must-Have Resource for Every Aspirant
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Repost from Python Courses & Resources
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Must watch "AI Engineer YouTube Playlist"
1. Neural Networks Zero to Hero (Karpathy) - https://lnkd.in/gBVSQqFf
2. Language Modelling from Scratch (Stanford CS336 2025) - https://lnkd.in/guuhQ8gA
3. Introduction to Deep Learning (MIT 6.S191 2025) - https://lnkd.in/ggBB_aCm
4. Introduction to Transformers (Talk - Andrej Karpathy) - https://lnkd.in/gYMTVVmH
5. Building LLMs (Stanford CS229 Guest Lecture) - https://lnkd.in/gP9xqXxi
6. Deep Dive into LLMs like ChatGPT - https://lnkd.in/gtZ9BAdA
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9. Transformers and Self-Attention - https://lnkd.in/gvZZtciU
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