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

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Programming, data science, ML - free courses by Big Data SpecialistNetwork:Programming, data science, ML - free courses by Big Data SpecialistIndia28 819Technologies & Applications8 911

πŸ“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 13 905 subscribers, ranking 8 911 in the Technologies & Applications category and 28 819 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 13 905 subscribers.

According to the latest data from 30 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 90 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.35%. Within the first 24 hours after publication, content typically collects 2.05% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 022 views. Within the first day, a publication typically gains 285 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.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€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 πŸ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

Thanks to the high frequency of updates (latest data received on 31 August, 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.

13 905
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Posts Archive
Data science/ML/AI
Data science/ML/AI
13 906
microsoft/Data-Science-For-Beginners Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'. Creator: Microsoft Stars ⭐️: 11.1k Forked By: 1.9k GithubRepo: https://github.com/microsoft/Data-Science-For-Beginners βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @github_repositories_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
Artificial Neural Networks (ANN) with Keras in Python and R Rating ⭐️: 4.7 out of 5 Duration ⏰: 11 hours on-demand video Students πŸ‘¨β€πŸ«: 143,495 Created by: Start-Tech Academy πŸ”— Course link Note: Free coupon is inserted in URL. Courses are FREE FOR FIRST 1000 enrollments #ai #ml #neural_networks #machine_learning #data_science #deep_learning βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
UDEMY FREE DEEP LEARNING COURSE Python for Deep Learning: Build Neural Networks in Python Rating ⭐️: 4.2 out of 5 Students πŸ‘¨β€πŸ«: 44,894 Created by: Meta Brains πŸ”— Course link Note: Free coupon is inserted in URL. Courses are FREE FOR 2 DAYS #python #datanalysis #datascience #deeplearing βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
photo content

Data science/ML/AI
Data science/ML/AI
13 906
Important Methods in Pandas
Important Methods in Pandas

Data science/ML/AI
Data science/ML/AI
13 906
Repost from Free programming books
The Hundred-Page Machine Learning Book by Andriy Burkov πŸ“„ 152 pages πŸ”— Book link #machinelearning #ml βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @pr
The Hundred-Page Machine Learning Book by Andriy Burkov πŸ“„ 152 pages πŸ”— Book link #machinelearning #ml βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @programming_books_bds for more

Data science/ML/AI
Data science/ML/AI
13 906
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 rateπŸ‘€ How can you handle these missing values, ensuring you dont loose important part of your dataπŸ€·β€β™€οΈ Not a problem😌. Here are important facts you must knowπŸ˜‰ ✍️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

Data science/ML/AI
Data science/ML/AI
13 906
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 βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @github_repositories_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
Data_Science_Cheatsheet.pdf1.69 MB

Data science/ML/AI
Data science/ML/AI
13 906
Data Preprocessing: Understanding and Detecting Outliers Here's a guide to understanding, detecting and handling outliersπŸ‘€.
Data Preprocessing: Understanding and Detecting Outliers Here's a guide to understanding, detecting and handling outliersπŸ‘€. 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😌 βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
How to choose chart for data visualization?
How to choose chart for data visualization?

Data science/ML/AI
Data science/ML/AI
13 906
UDEMY FREE DATA MANIPULATION AND DEEP LEARNING COURSES 1) Data Manipulation in Python: Master Python, Numpy & Pandas Rating ⭐️: 4.3 out of 5 Students πŸ‘¨β€πŸ«: 80,451 Created by: Meta Brains πŸ”— Course link 2) Python for Deep Learning: Build Neural Networks in Python Rating ⭐️: 4.2 out of 5 Students πŸ‘¨β€πŸ«: 44,128 Created by: Meta Brains πŸ”— Course link Note: Free coupon is inserted in URL. Courses are FREE FOR 3 DAYS #python #datanalysis #datascience #deeplearing #numpy #pandas βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
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) Γ· 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) Γ· 2 = 10 Γ· 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
13 906
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
13 906
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 βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
photo content

Data science/ML/AI
Data science/ML/AI
13 906
Facts you need to know about GPUs for Deep Learning Have you heard about GPUs?πŸ€“ What is GPU and why should i care?🀨 Well I
Facts you need to know about GPUs for Deep Learning Have you heard about GPUs?πŸ€“ What is GPU and why should i care?🀨 Well I know you might be wondering what this has to do with your deep learning projectsπŸ˜‰ 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, πŸ‘ŒIt enables the distribution of training processes and can significantly speed machine learning operations. πŸ‘ŒIt is a safer bet for quick deep learning since data science model training is based on simple matrix arithmetic calculations. πŸ‘ŒTraining models is a hardware-intensive operation, and a good GPU will ensure that neural network operations operate smoothly. πŸ‘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
13 906
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
13 906
Repost from Free programming books
Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman πŸ“„ 603 pages #Data_Mining #Datasets βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @programming_books_bds for more

Data science/ML/AI
Data science/ML/AI
13 906
K-Means clustering explained
K-Means clustering explained