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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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📈 Analytical overview of Telegram channel Data Science & Machine Learning

Channel Data Science & Machine Learning (@datasciencefun) in the English language segment is an active participant. Currently, the community unites 75 833 subscribers, ranking 2 106 in the Education category and 4 234 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 75 833 subscribers.

According to the latest data from 21 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 770 over the last 30 days and by 8 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 3.15%. Within the first 24 hours after publication, content typically collects 1.09% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 385 views. Within the first day, a publication typically gains 827 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
  • Thematic interests: Content is focused on key topics such as learning, accuracy, distribution, panda, dataset.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
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

Thanks to the high frequency of updates (latest data received on 22 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 Education category.

75 833
Subscribers
+824 hours
+717 days
+77030 days
Posts Archive
+1
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

Machine_Learning_andrewng.pdf4.01 MB

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!

+2
GIT Cheatsheet.pdf0.70 KB

Data Engineering with AWS PDF

Big data notes.pdf2.89 MB

#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, :] = # ...

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

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

Pattern Recognition and Machine Learning.pdf4.52 MB

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

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!

SQL handwritten notes .pdf1.37 MB

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

Numpy_Python_Cheat_Sheet.pdf6.49 KB

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