Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Data Science & Machine Learning
Kanalga Telegram’da o‘tish
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
Ko'proq ko'rsatish📈 Telegram kanali Data Science & Machine Learning analitikasi
Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 833 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, 75 833 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.
75 833
Obunachilar
+824 soatlar
+717 kunlar
+77030 kunlar
Postlar arxiv
Which of the following tool can be used for data visualization?
Fundamentals_of_Deep_Learning,_2nd_Edition_Fourth_Early_Release.pdf10.94 MB
New Data Scientists - When you learn, it's easy to get distracted by Machine Learning & Deep Learning terms like "XGBoost", "Neural Networks", "RNN", "LSTM" or Advanced Technologies like "Spark", "Julia", "Scala", "Go", etc.
Don't get bogged down trying to learn every new term & technology you come across.
Instead, focus on foundations.
- data wrangling
- visualizing
- exploring
- modeling
- understanding the results.
The best tools are often basic, Build yourself up. You'll advance much faster. Keep learning!
Machine Learning Mastery with Python: Understand Your Data, Create Accurate Models and Work Projects End-to-end PDF
💥Deep Learning with Pytorch by Prof.Yann LeCun (CNN Founder)
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
GitHub Link: https://atcold.github.io/pytorch-Deep-Learning/
YouTube Playlist: https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq
Top 8 Github Repos to Learn Data Science and Python
1. All algorithms implemented in Python
By: The Algorithms
Stars ⭐️: 135K
Fork: 35.3K
Repo: https://github.com/TheAlgorithms/Python
2. DataScienceResources
By: jJonathan Bower
Stars ⭐️: 3K
Fork: 1.3K
Repo: https://github.com/jonathan-bower/DataScienceResources
3. Playground and Cheatsheet for Learning Python
By: Oleksii Trekhleb ( Also the Image)
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Fork: 2K
Repo: https://github.com/trekhleb/learn-python
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Repo: https://github.com/jerry-git/learn-python3
5. Awesome Data Science
By: Fatih Aktürk, Hüseyin Mert & Osman Ungur, Recep Erol.
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Repo: https://github.com/academic/awesome-datascience
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Repo: https://github.com/MrMimic/data-scientist-roadmap
7. Data Science Best Resources
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Repo: https://github.com/tirthajyoti/Data-science-best-resources/blob/master/README.md
8. Ds-cheatsheets
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Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals PDF
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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
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
