Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Data science/ML/AI
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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
Ko'proq ko'rsatish📈 Telegram kanali Data science/ML/AI analitikasi
Data science/ML/AI (@datascience_bds) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 13 684 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 9 384-o'rinni va Hindiston mintaqasida 31 551-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 13 684 obunachiga ega bo‘ldi.
11 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 150 ga, so‘nggi 24 soatda esa 11 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 8.13% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.20% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 112 marta ko‘riladi; birinchi sutkada odatda 301 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 5 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent panda, learning, row, api, ethic kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Data science and machine learning hub
Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources.
For beginners, data scientists and ML engineers
👉 https://rebrand.ly/bigdatachannels
DMCA: @disclosure_bds
Contact: @mldatasci...”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 12 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
13 684
Obunachilar
+1124 soatlar
+227 kunlar
+15030 kunlar
Postlar arxiv
13 690
Repost from Free programming books
The Hundred-Page Machine Learning Book
by Andriy Burkov
📄 152 pages
🔗 Book link
#machinelearning #ml
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Join @programming_books_bds for more
13 690
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
13 690
Repost from Cool GitHub repositories
labmlai/annotated_deep_learning_paper_implementatios
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations
Creator: labml.ai
Stars ⭐️: 7.8k
Forked By: 703
GithubRepo: https://github.com/labmlai/annotated_deep_learning_paper_implementations
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Join @github_repositories_bds for more cool repositories.
*This channel belongs to @bigdataspecialist group
13 690
Data Preprocessing: Understanding and Detecting Outliers
Here's a guide to understanding, detecting and handling outliers👀.
I hope you gain the confidence you need to handle them😁
Outlier Detection and Analysis Methods
Link: Click Me 😌
Detecting and Treating Outliers | Treating the odd one out!
Link: Click Me 😌
Python Treatment for Outliers in Data Science
Link: Click Me 😌
Why You Shouldn’t Just Delete Outliers
Link: Click Me😌
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13 690
UDEMY FREE DATA MANIPULATION AND DEEP LEARNING COURSES
1) Data Manipulation in Python: Master Python, Numpy & Pandas
Rating ⭐️: 4.3 out of 5
Students 👨🏫: 80,451
Created by: Meta Brains
🔗 Course link
2) Python for Deep Learning: Build Neural Networks in Python
Rating ⭐️: 4.2 out of 5
Students 👨🏫: 44,128
Created by: Meta Brains
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR 3 DAYS
#python #datanalysis #datascience #deeplearing #numpy #pandas
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13 690
Let's talk about some simple stat terms - mean, median and mode
Mean, median, and mode are three kinds of "averages". There are many "averages" in statistics, but these are, I think, the three most common, and are certainly the three you are most likely to encounter in your pre-statistics courses, if the topic comes up at all.
The "mean" is the "average" you're used to, where you add up all the numbers and then divide by the number of numbers.
The "median" is the "middle" value in the list of numbers. To find the median, your numbers have to be listed in numerical order from smallest to largest, so you may have to rewrite your list before you can find the median.
The "mode" is the value that occurs most often. If no number in the list is repeated, then there is no mode for the list.
Task:
Find the mean, median, mode, and range for the following list of values:
13, 18, 13, 14, 13, 16, 14, 21, 13
Solution:
mean: 15
median: 14
mode: 13
Explanation:
The mean is the usual average, so I'll add and then divide:
(13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) ÷ 9 = 15
The median is the middle value, so first I'll have to rewrite the list in numerical order:
13, 13, 13, 13, 14, 14, 16, 18, 21
There are nine numbers in the list, so the middle one will be the (9 + 1) ÷ 2 = 10 ÷ 2 = 5th number: 14
The mode is the number that is repeated more often than any other, so 13 is the mode.
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Machine learning for dummies
IBMs limited edition
Judith Hurwitz
Daniel Kirsch
https://www.ibm.com/downloads/cas/GB8ZMQZ3
13 690
Awesome Public Datasets for Your Projects
This contains numerous datasets ranging from :
Agriculture
Biology
Climate+Weather
Complex Networks
Computer Networks
Cyber Security
Data Challenges
Earth Science
Economics
Education
Energy
Entertainment
Finance
...
There's alot you can lay your hands on here
Stars⭐️: 48.8K
Fork: 8.7K
Repo: https://github.com/awesomedata/awesome-public-datasets
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Join @datascience_bds for more cool data science materials.
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13 690
Facts you need to know about GPUs for Deep Learning
Have you heard about GPUs?🤓 What is GPU and why should i care?🤨
Well I know you might be wondering what this has to do with your deep learning projects😉
Graphics Processing Units (GPUs) are specialized processing cores that you can use to speed computational processes.
It was initially designed to process images and visual data. But now, It is used in reducing the efficiency and power needed to run DL projects,
👌It enables the distribution of training processes and can significantly speed machine learning operations.
👌It is a safer bet for quick deep learning since data science model training is based on simple matrix arithmetic calculations.
👌Training models is a hardware-intensive operation, and a good GPU will ensure that neural network operations operate smoothly.
👌It has a good Video RAM,which frees up CPU for other tasks and providing necessary memory bandwidth for huge datasets.
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Structured vs unstructured data
It is useful to distinguish between structured and unstructured data. The former is typically represented in some well-structured form, often as a table or number of tables, while the latter is just a collection of files. Sometimes we can also talk about semi-structured data, that have some sort of a structure
that may vary greatly.
13 690
Repost from Free programming books
Mining of Massive Datasets
by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman
📄 603 pages
#Data_Mining #Datasets
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13 690
20 AWESOME SOURCES OF FREE DATA SETS
If you are after solid data to do your projects with ease and lessen the stress of doing the data collection yourself, here's a good resource containing amazing sites where you can get your data sets for free😁
https://www.searchenginejournal.com/free-data-sources/302601/#close
