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Machine Learning & Artificial Intelligence | Data Science Free Courses
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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
Ko'proq ko'rsatish📈 Telegram kanali Machine Learning & Artificial Intelligence | Data Science Free Courses analitikasi
Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 66 676 obunachidan iborat bo'lib, Taʼlim toifasida 2 463-o'rinni va Malayziya mintaqasida 433-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 66 676 obunachiga ega bo‘ldi.
22 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 518 ga, so‘nggi 24 soatda esa -9 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 0.86% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.79% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 572 marta ko‘riladi; birinchi sutkada odatda 524 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 4 ta reaksiya keladi.
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Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 23 Iyun, 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.
66 676
Obunachilar
-924 soatlar
+77 kunlar
+51830 kunlar
Postlar arxiv
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✅ Data Science Mistakes Beginners Should Avoid ⚠️📉
1️⃣ Skipping the Basics
• Jumping into ML without Python, Stats, or Pandas
✅ Build strong foundations in math, programming & EDA first
2️⃣ Not Understanding the Problem
• Applying models blindly
• Irrelevant features and metrics
✅ Always clarify business goals before coding
3️⃣ Treating Data Cleaning as Optional
• Training on dirty/incomplete data
✅ Spend time on preprocessing — it’s 70% of real work
4️⃣ Using Complex Models Too Early
• Overfitting small datasets
• Ignoring simpler, interpretable models
✅ Start with baseline models (Logistic Regression, Decision Trees)
5️⃣ No Evaluation Strategy
• Relying only on accuracy
✅ Use proper metrics (F1, AUC, MAE) based on problem type
6️⃣ Not Visualizing Data
• Missed outliers and patterns
✅ Use Seaborn, Matplotlib, Plotly for EDA
7️⃣ Poor Feature Engineering
• Feeding raw data into models
✅ Create meaningful features that boost performance
8️⃣ Ignoring Domain Knowledge
• Features don’t align with real-world logic
✅ Talk to stakeholders or do research before modeling
9️⃣ No Practice with Real Datasets
• Kaggle-only learning
✅ Work with messy, real-world data (open data portals, APIs)
🔟 Not Documenting or Sharing Work
• No GitHub, no portfolio
✅ Document notebooks, write blogs, push projects online
💬 Tap ❤️ for more!
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