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Coding_knowledge

Coding_knowledge

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πŸ’‘ Your Coding Journey Starts Here! Get free courses, coding resources, internships, job updates & much more. Stay ahead in tech with us! β€οΈπŸš€ Join our WhatsApp groupπŸ‘‡ https://whatsapp.com/channel/0029Vaa7CVhCRs1rxJzy1n3D

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Coding_knowledge (@coding_knwledge01) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 77 031 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 1 668-o'rinni va Hindiston mintaqasida 3 875-o'rinni egallagan.

πŸ“Š Auditoriya koβ€˜rsatkichlari va dinamika

Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ sanasidan buyon loyiha tez oβ€˜sib, 77 031 obunachiga ega boβ€˜ldi.

07 Noyabr, 2026 dagi oxirgi ma’lumotlarga koβ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -1 098 ga, soβ€˜nggi 24 soatda esa -44 ga oβ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

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πŸ“ Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
β€œπŸ’‘ Your Coding Journey Starts Here! Get free courses, coding resources, internships, job updates & much more. Stay ahead in tech with us! β€οΈπŸš€ Join our WhatsApp groupπŸ‘‡ https://whatsapp.com/channel/0029Vaa7CVhCRs1rxJzy1n3D”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 08 Noyabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boβ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini koβ€˜rsatadi.

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Postlar arxiv
Top 150 companies DSA questions with answers πŸš€ . . Click here πŸ‘‡:- https://drive.google.com/drive/folders/1NlbJI1MAfb4UfL5h5AoaeO6-UlA3hF22 Don't forget to react to this message to get more content like this πŸš€πŸ§‘β€πŸ’»

Dynamic Programming Notes πŸ’‘

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Python Tricks ⭐

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AI/ML RoadmapπŸ‘¨πŸ»β€πŸ’»πŸ‘ΎπŸ€– - ==== Step 1: Basics ==== πŸ“Š Learn Math (Linear Algebra, Probability). πŸ€” Understand AI/ML Fundamentals (Supervised vs Unsupervised). ==== Step 2: Machine Learning ==== πŸ”’ Clean & Visualize Data (Pandas, Matplotlib). πŸ‹οΈβ€β™‚οΈ Learn Core Algorithms (Linear Regression, Decision Trees). πŸ“¦ Use scikit-learn to implement models. ==== Step 3: Deep Learning ==== πŸ’‘ Understand Neural Networks. πŸ–ΌοΈ Learn TensorFlow or PyTorch. πŸ€– Build small projects (Image Classifier, Chatbot). ==== Step 4: Advanced Topics ==== 🌳 Study Advanced Algorithms (Random Forest, XGBoost). πŸ—£οΈ Dive into NLP or Computer Vision. πŸ•ΉοΈ Explore Reinforcement Learning. ==== Step 5: Build & Share ==== 🎨 Create real-world projects. 🌍 Deploy with Flask, FastAPI, or Cloud Platforms. #techinfo @coding_knwledge01

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