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Data science/ML/AI

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 ๐Ÿ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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๐Ÿ“ˆ Analytical overview of Telegram channel Data science/ML/AI

Channel Data science/ML/AI (@datascience_bds) in the English language segment is an active participant. Currently, the community unites 13 684 subscribers, ranking 9 384 in the Technologies & Applications category and 31 551 in the India region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 13 684 subscribers.

According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 150 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 8.13%. Within the first 24 hours after publication, content typically collects 2.20% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 112 views. Within the first day, a publication typically gains 301 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
  • Thematic interests: Content is focused on key topics such as panda, learning, row, api, ethic.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œData science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers ๐Ÿ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...โ€

Thanks to the high frequency of updates (latest data received on 12 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

13 684
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Posts Archive
Data science/ML/AI
Data science/ML/AI
13 690
photo content

Data science/ML/AI
Data science/ML/AI
13 690
Important Methods in Pandas
Important Methods in Pandas

Data science/ML/AI
Data science/ML/AI
13 690
Repost from Free programming books
The Hundred-Page Machine Learning Book by Andriy Burkov ๐Ÿ“„ 152 pages ๐Ÿ”— Book link #machinelearning #ml โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– Join @pr
The Hundred-Page Machine Learning Book by Andriy Burkov ๐Ÿ“„ 152 pages ๐Ÿ”— Book link #machinelearning #ml โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– Join @programming_books_bds for more

Data science/ML/AI
Data science/ML/AI
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 rate๐Ÿ‘€ How can you handle these missing values, ensuring you dont loose important part of your data๐Ÿคทโ€โ™€๏ธ Not a problem๐Ÿ˜Œ. Here are important facts you must know๐Ÿ˜‰ โœ๏ธInstances with missing values for all features should be eliminated โœ๏ธFeatures with high absence rate should either be eliminated or filled with values โœ๏ธMissing values can be replaced using Mean Imputation or Regression Imputation โœ๏ธ Be careful with mean imputation for it may introduce bias as it evens out all instances โœ๏ธRegression Imputation might overfit your model โœ๏ธMean and Regression Imputation can't be applied to Text features with missing values โœ๏ธText Features with missing values can be eliminated if not needed in data โœ๏ธImportant Text Features with Missing values can be replaced with a new class or category labelled as uncategorized

Data science/ML/AI
Data science/ML/AI
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 โญ๏ธ: 7.8k Forked By: 703 GithubRepo: https://github.com/labmlai/annotated_deep_learning_paper_implementations โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– Join @github_repositories_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 690
Data_Science_Cheatsheet.pdf1.69 MB

Data science/ML/AI
Data science/ML/AI
13 690
Data Preprocessing: Understanding and Detecting Outliers Here's a guide to understanding, detecting and handling outliers๐Ÿ‘€.
Data Preprocessing: Understanding and Detecting Outliers Here's a guide to understanding, detecting and handling outliers๐Ÿ‘€. I hope you gain the confidence you need to handle them๐Ÿ˜ Outlier Detection and Analysis Methods Link: Click Me ๐Ÿ˜Œ Detecting and Treating Outliers | Treating the odd one out! Link: Click Me ๐Ÿ˜Œ Python Treatment for Outliers in Data Science Link: Click Me ๐Ÿ˜Œ Why You Shouldnโ€™t Just Delete Outliers Link: Click Me๐Ÿ˜Œ โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 690
How to choose chart for data visualization?
How to choose chart for data visualization?

Data science/ML/AI
Data science/ML/AI
13 690
UDEMY FREE DATA MANIPULATION AND DEEP LEARNING COURSES 1) Data Manipulation in Python: Master Python, Numpy & Pandas Rating โญ๏ธ: 4.3 out of 5 Students ๐Ÿ‘จโ€๐Ÿซ: 80,451 Created by: Meta Brains ๐Ÿ”— Course link 2) Python for Deep Learning: Build Neural Networks in Python Rating โญ๏ธ: 4.2 out of 5 Students ๐Ÿ‘จโ€๐Ÿซ: 44,128 Created by: Meta Brains ๐Ÿ”— Course link Note: Free coupon is inserted in URL. Courses are FREE FOR 3 DAYS #python #datanalysis #datascience #deeplearing #numpy #pandas โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
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) รท 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) รท 2 = 10 รท 2 = 5th number: 14 The mode is the number that is repeated more often than any other, so 13 is the mode.

Data science/ML/AI
Data science/ML/AI
13 690
Machine learning for dummies IBMs limited edition Judith Hurwitz Daniel Kirsch https://www.ibm.com/downloads/cas/GB8ZMQZ3
Machine learning for dummies IBMs limited edition Judith Hurwitz Daniel Kirsch https://www.ibm.com/downloads/cas/GB8ZMQZ3

Data science/ML/AI
Data science/ML/AI
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 Starsโญ๏ธ: 48.8K Fork: 8.7K Repo: https://github.com/awesomedata/awesome-public-datasets โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 690
photo content

Data science/ML/AI
Data science/ML/AI
13 690
Facts you need to know about GPUs for Deep Learning Have you heard about GPUs?๐Ÿค“ What is GPU and why should i care?๐Ÿคจ Well I
Facts you need to know about GPUs for Deep Learning Have you heard about GPUs?๐Ÿค“ What is GPU and why should i care?๐Ÿคจ Well I know you might be wondering what this has to do with your deep learning projects๐Ÿ˜‰ 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, ๐Ÿ‘ŒIt enables the distribution of training processes and can significantly speed machine learning operations. ๐Ÿ‘ŒIt is a safer bet for quick deep learning since data science model training is based on simple matrix arithmetic calculations. ๐Ÿ‘ŒTraining models is a hardware-intensive operation, and a good GPU will ensure that neural network operations operate smoothly. ๐Ÿ‘ŒIt has a good Video RAM,which frees up CPU for other tasks and providing necessary memory bandwidth for huge datasets.

Data science/ML/AI
Data science/ML/AI
13 690
Structured vs unstructured data It is useful to distinguish between structured and unstructured data. The former is typically
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.

Data science/ML/AI
Data science/ML/AI
13 690
Repost from Free programming books
Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman ๐Ÿ“„ 603 pages #Data_Mining #Datasets โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– Join @programming_books_bds for more

Data science/ML/AI
Data science/ML/AI
13 690
K-Means clustering explained
K-Means clustering explained

Data science/ML/AI
Data science/ML/AI
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 free๐Ÿ˜ https://www.searchenginejournal.com/free-data-sources/302601/#close

Data science/ML/AI
Data science/ML/AI
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THE MACHINE LEARNING DEVELOPMENT WORKFLOW
THE MACHINE LEARNING DEVELOPMENT WORKFLOW

Data science/ML/AI
Data science/ML/AI
13 690
photo content