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Data Science & Machine Learning

Data Science & Machine Learning

الذهاب إلى القناة على Telegram

Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data

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Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expertالشبكة:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expertالهند4 204التعليم2 103

📈 نظرة تحليلية على قناة تيليجرام Data Science & Machine Learning

تُعد قناة Data Science & Machine Learning (@datasciencefun) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 75 933 مشتركاً، محتلاً المرتبة 2 103 في فئة التعليم والمرتبة 4 204 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 75 933 مشتركاً.

بحسب آخر البيانات بتاريخ 23 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 731، وفي آخر 24 ساعة بمقدار 33، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 2.95‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 0.86‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 2 239 مشاهدة. وخلال اليوم الأول يجمع عادةً 650 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 3.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل learning, accuracy, distribution, panda, dataset.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data”

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 24 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.

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Data Science & Machine Learning
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F-beta score is always
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Data Science & Machine Learning
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Skills Required For A Data Analyst in 2021 Basic Excel-  https://www.youtube.com/playlist?list=PLmQAMKHKeLZ_ADx6nJcoTM5t2S1bmsMdm Advanced Excel-  https://www.youtube.com/playlist?list=PLmQAMKHKeLZ_e9xmZNPACsLdgie3Tkaxf SQL- https://www.youtube.com/watch?v=5JCyiutyu_o&list=PLmQAMKHKeLZ-kD9VN0prfKCByr9pa4jw6 SQL- (Khan Academy)- https://www.khanacademy.org/computing/computer-programming/sql Python Programming Language-  https://www.youtube.com/watch?v=bPrmA1SEN2k&list=PLZoTAELRMXVNUL99R4bDlVYsncUNvwUBB Stats Lectures-  https://www.youtube.com/watch?v=zRUliXuwJCQ&list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO Stats Lectures(Khans Academy)- https://www.khanacademy.org/math/statistics-probability Python EDA-  https://www.youtube.com/playlist?list=PLZoTAELRMXVPQyArDHyQVjQxjj_YmEuO9 Python Feature Engineering-  https://www.youtube.com/playlist?list=PLZoTAELRMXVPwYGE2PXD3x0bfKnR0cJjN Tableau-  https://www.tableau.com/academic/student-Iron-Viz

Data Science & Machine Learning
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👉A handy notebook on handling missing values Link : 👇👇 https://www.kaggle.com/parulpandey/a-guide-to-handling-missing-values-in-python A list of NLP Tutorials Link : 👇👇 https://github.com/lyeoni/nlp-tutorial “An Implementation and Explanation of the Random Forest in Python” by Will Koehrsen 👇👇 https://link.medium.com/GCWFv81v95 “How to analyse 100s of GBs of data on your laptop with Python” by Jovan Veljanoski 👇👇 https://link.medium.com/V8xS82Cax6

Data Science & Machine Learning
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75 933
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Scatter plot is used to?
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Recall how many of the true positives were recalled (found), i.e. how many of the correct hits were also found. Its formula would be
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Precision is one indicator of a machine learning model's performance – the quality of a positive prediction made by the model. Its formula would be?
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Type-2 error is?
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Type-1 Error is?
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Data Science & Machine Learning
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Seeing Theory : A visual introduction to probability and statistics Link :👇👇 https://seeing-theory.brown.edu/ “The Projects You Should Do to Get a Data Science Job” by Ken Jee 👇👇 https://link.medium.com/Q2DnxSGRO6

Data Science & Machine Learning
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👉The Ultimate Guide to the Pandas Library for Data Science in Python 👇👇 https://www.freecodecamp.org/news/the-ultimate-guide-to-the-pandas-library-for-data-science-in-python/amp/ A Visual Intro to NumPy and Data Representation . Link : 👇👇 https://jalammar.github.io/visual-numpy/ Matplotlib Cheatsheet 👇👇 https://github.com/rougier/matplotlib-cheatsheet SQL Cheatsheet 👇👇 https://websitesetup.org/sql-cheat-sheet/

Data Science & Machine Learning
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Start working on any project if you are a beginner and want to grow your career as a data scientist You will learn much more as you practice and work on projects from yourself You can find dataset in this channel or go to kaggle to find any random dataset and just work on it Learning concepts is fine but most of the learnings come from projects I know that might feel boring at first time but as you move forward, it become interesting

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K-means vs DBScan ML Algorithm DBScan is more robust to noise. DBScan is better when the amount of clusters is difficult to guess. K-means has a lower complexity, i.e. it will be much faster, especially with a larger amount of points.

Data Science & Machine Learning
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What is the curse of dimensionality? Why do we care about it? Data in only one dimension is relatively tightly packed. Adding a dimension stretches the points across that dimension, pushing them further apart. Additional dimensions spread the data even further making high dimensional data extremely sparse. We care about it, because it is difficult to use machine learning in sparse spaces.

Data Science & Machine Learning
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Dimensionality reduction techniques Singular Value Decomposition (SVD) Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA) T-distributed Stochastic Neighbor Embedding (t-SNE) Autoencoders Fourier and Wavelet Transforms

Data Science & Machine Learning
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Data_science Numpy cheat sheet

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Chatbot project using ML Before using this you have to install Tensorflow, keras , pickle, nltk by using pip install in command prompt

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Pandas