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@byname906: By shuk###

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Wednesday 07 October 2026 07:53:24 GMT
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jolimashmalow
Rajpoot Kanwal :
bashak
2026-10-07 08:56:53
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jolimashmalow
Rajpoot Kanwal :
InshaAllah
2026-10-07 08:57:10
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jolimashmalow
Rajpoot Kanwal :
InshaAllah zharor
2026-10-07 08:57:15
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jolimashmalow
Rajpoot Kanwal :
🥰🥰🥰
2026-10-07 08:56:57
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iftikha068
Iftikhar :
🥰🥰🥰
2026-10-07 08:33:31
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cario2816
𝑴𝒖𝒉𝒂𝒎𝒎𝒂𝒅 𝑺𝒉𝒐𝒂𝒊𝒃 :
[Red heart][Red heart][Red heart]
2026-10-07 10:20:47
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#рек #☝🏻🤲🏾🕋😔💔🥀🖤 #🥺💔
#рек #☝🏻🤲🏾🕋😔💔🥀🖤 #🥺💔
أعوذ بالله من قلوب يملؤوها مرض الحسد اللهم أكفنا شر خلقك وأشغلهم في أنفسهم . #يارب #دعاء_يريح_القلوب #اكسبلور #دعاء #بسم_الله_الذي_لايضر_مع_اسمه_شيء
أعوذ بالله من قلوب يملؤوها مرض الحسد اللهم أكفنا شر خلقك وأشغلهم في أنفسهم . #يارب #دعاء_يريح_القلوب #اكسبلور #دعاء #بسم_الله_الذي_لايضر_مع_اسمه_شيء


#لين_محمد
#لين_محمد
mufti menhk motivation speech #mufti_menk #motivation #muftimenkofficial #islmic_video #muftimenktiktok
mufti menhk motivation speech #mufti_menk #motivation #muftimenkofficial #islmic_video #muftimenktiktok
🚀 Machine Learning: Best Libraries for Data Science (You Must Know) If you want real data science & machine learning jobs, these libraries are non-negotiable: 1️⃣ NumPy The foundation of ML. Fast arrays, linear algebra, and numerical operations. 2️⃣ Pandas Real-world data is messy. Pandas helps you clean, analyze, and prepare datasets. 3️⃣ Matplotlib / Seaborn Data science is useless if you can’t explain insights visually. 4️⃣ Scikit-learn Your ML toolbox. Regression, classification, clustering, evaluation, pipelines. 5️⃣ TensorFlow / PyTorch For deep learning & AI. Used in industry, research, and production systems. 6️⃣ XGBoost / LightGBM If you want to win competitions and build strong tabular models. 📈 Learn these properly and you’re already ahead of 80% of beginners. 💬 Comment Booster (Very Important): Which library gave you the most trouble when learning? A) NumPy B) Pandas C) Scikit-learn D) Deep Learning (TF / PyTorch) 👇 Comment the letter. I’ll reply with a learning tip. #creatorsearchinsights #machinelearning #datascience #datasciencelibraries #python
🚀 Machine Learning: Best Libraries for Data Science (You Must Know) If you want real data science & machine learning jobs, these libraries are non-negotiable: 1️⃣ NumPy The foundation of ML. Fast arrays, linear algebra, and numerical operations. 2️⃣ Pandas Real-world data is messy. Pandas helps you clean, analyze, and prepare datasets. 3️⃣ Matplotlib / Seaborn Data science is useless if you can’t explain insights visually. 4️⃣ Scikit-learn Your ML toolbox. Regression, classification, clustering, evaluation, pipelines. 5️⃣ TensorFlow / PyTorch For deep learning & AI. Used in industry, research, and production systems. 6️⃣ XGBoost / LightGBM If you want to win competitions and build strong tabular models. 📈 Learn these properly and you’re already ahead of 80% of beginners. 💬 Comment Booster (Very Important): Which library gave you the most trouble when learning? A) NumPy B) Pandas C) Scikit-learn D) Deep Learning (TF / PyTorch) 👇 Comment the letter. I’ll reply with a learning tip. #creatorsearchinsights #machinelearning #datascience #datasciencelibraries #python

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