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Data Science & Machine Learning

Data Science & Machine Learning

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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 ExpertTarmoq:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertHindiston4 234Taʼlim2 106

📈 Telegram kanali Data Science & Machine Learning analitikasi

Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 831 obunachidan iborat bo'lib, Taʼlim toifasida 2 106-o'rinni va Hindiston mintaqasida 4 234-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 75 831 obunachiga ega bo‘ldi.

21 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 770 ga, so‘nggi 24 soatda esa 8 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 3.15% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.09% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 2 385 marta ko‘riladi; birinchi sutkada odatda 827 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent learning, accuracy, distribution, panda, dataset kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 22 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.

75 831
Obunachilar
+824 soatlar
+717 kunlar
+77030 kunlar
Postlar arxiv
Data Science & Machine Learning
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Python for Data Science: The Ultimate Step-by-Step Guide to Learn Python In 7 Days & NLP, Data Science from with Python PDF

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A LITTLE GUIDE TO HANDLING MISSING DATA Having any Feature missing more than 5-10% of its values? you should consider it to be missing data or feature with high absence rate👀 How can you handle these missing values, ensuring you dont loose important part of your data🤷‍♀️ Not a problem😌. Here are important facts you must know😉 ✍️Instances with missing values for all features should be eliminated ✍️Features with high absence rate should either be eliminated or filled with values ✍️Missing values can be replaced using Mean Imputation or Regression Imputation ✍️ Be careful with mean imputation for it may introduce bias as it evens out all instances ✍️Regression Imputation might overfit your model ✍️Mean and Regression Imputation can't be applied to Text features with missing values ✍️Text Features with missing values can be eliminated if not needed in data ✍️Important Text Features with Missing values can be replaced with a new class or category labelled as uncategorized

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Machine_Learning_andrewng.pdf4.01 MB

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To become a Machine Learning Engineer: • Python • numpy, pandas, matplotlib, Scikit-Learn • TensorFlow or PyTorch • Jupyter, Colab • Analysis > Code • 99%: Foundational algorithms • 1%: Other algorithms • Solve problems ← This is key • Teaching = 2 × Learning • Have fun!

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GIT Cheatsheet.pdf0.70 KB

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Data Engineering with AWS PDF

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Big data notes.pdf2.89 MB

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#numpy NumPy Smart use of ‘:’ to extract the right shape Sometimes you encounter a 3-dim array that is of shape (N, T, D), while your function requires a shape of (N, D). At a time like this, reshape() will do more harm than good, so you are left with one simple solution: Example: for t in xrange(T): x[:, t, :] = # ...

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📕 Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan University Press, Cambridge

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thebook_ Introduction to Machine Learning.pdf10.31 MB

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Expert_Python_Programming_Master_Python_by_learning_the_best_coding.epub4.52 MB

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Pattern Recognition and Machine Learning.pdf4.52 MB

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Pattern Recognition and Machine Learning [ Information Science and Statistics ] Christopher M. Bishop #python #machinelearning #statistics #information #ai #ml

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Follow the latest IT, computer science and entrepreneurship news on Hacker News Digest Telegram channel Hacker News (news.ycombinator.com) – is one of the most influential social news websites. It was here that Drew Houston first introduced Dropbox to the world.   Hacker News Digest Telegram channel will send you the top 10 most popular posts from Hacker News, daily. Subscribe to stay up-to-date!

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SQL handwritten notes .pdf1.37 MB

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SQL Tips and Tricks for Data Science.zip147.63 MB

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Numpy_Python_Cheat_Sheet.pdf6.49 KB

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Want more free courses like this?
Anonymous voting

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Data Visualisation Cheatsheet 🚀

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9 Best Machine Learning Use cases in our Daily Lives 🚀 👓 Youtube Recommendation 👓 Voice Assistants 👓 arrow Smartphone Cam
9 Best Machine Learning Use cases in our Daily Lives 🚀 👓 Youtube Recommendation 👓 Voice Assistants 👓 arrow Smartphone Camera 👓 Google Maps routes 👓 Email Filtering 👓 Search 👓 Translation 👓 Chatbots 👓 Fraud Protection