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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

Network: Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertIndia3 825Education2 015...

📈 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 77 661 subscribers, ranking 2 015 in the Education category and 3 825 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 77 661 subscribers.

According to the latest data from 12 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -123 over the last 30 days and by -21 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.52%. Within the first 24 hours after publication, content typically collects 1.01% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 954 views. Within the first day, a publication typically gains 781 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 13 November, 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.

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77 661

Subscribers

-2124 hours

-767 days

-12330 days

1 954

Post views

~ 78124 hours

~ 1 03648 hours

2.52%

Engagement rate

~ 2

Posts per day

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Posts Archive

77 661

Numpy_Python_Cheat_Sheet.pdf6.49 KB

77 661

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77 661

Data Visualisation Cheatsheet 🚀

77 661

9 Best Machine Learning Use cases in our Daily Lives 🚀 👓 Youtube Recommendation 👓 Voice Assistants 👓 arrow Smartphone Camera 👓 Google Maps routes 👓 Email Filtering 👓 Search 👓 Translation 👓 Chatbots 👓 Fraud Protection

77 661

Introduction_to_Machine_Learning_with_Python_A_Guide_for_Beginners.epub1.95 MB

77 661

Machine Learning CheatSheet.pdf3.44 MB

77 661

Machine Learning Cheatsheet #python #ml #cheatsheet #ai

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🚀Join our first free lessons and explore the fields of tech! You will find the answers to all your questions at our webinars. Open the link https://crst.co/imKOy, make your choice and apply now while there are still seats available. See you there! ▶️ May 4 - Manual QA Course. Free first lesson! ▶️ May 10 - Sales Engineer Course. Free first lesson!▶️ May 23 - Tech Sales Training. Free first lesson! ▶️ June 2 - Systems Engineer. Free first lesson! Special offer for all participants! ️✅ Apply by the link https://crst.co/hHZ9a

77 661

Repost from Machine Learning & Artificial Intelligence | Data Science Free Courses

TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

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Machine Learning Notes - TutorialsDuniya.pdf14.65 MB

77 661

You are given a data set. The data set has missing values which spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why? Answer: This question has enough hints for you to start thinking! Since, the data is spread across median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.

77 661

Repost from Machine Learning & Artificial Intelligence | Data Science Free Courses

🔰 Python for Machine Learning & Data Science Masterclass ⏱ 44 Hours 📦 170 Lessons Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more! Taught By: Jose Portilla Download Full Course: https://t.me/datasciencefree/69 Download All Courses: https://t.me/datasciencefree/2

77 661

Jeroen_Janssens_Data_Science_at_the_Command_Line_Obtain,_Scrub,.epub7.96 MB

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Core machine learning concepts explained through memes and simple charts created by Mihail Eric.

77 661

Natural_Language_Processing_with_Python_by_Steven_Bird,_Ewan_Klein.pdf5.18 MB

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Machine-Learning-With-Python-For-Everyone-Pearson-2020.pdf9.00 MB

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DATA SCIENCE INTERVIEW QUESTIONS WITH ANSWERS 1. What is a logistic function? What is the range of values of a logistic function? f(z) = 1/(1+e -z ) The values of a logistic function will range from 0 to 1. The values of Z will vary from -infinity to +infinity. 2. What is the difference between R square and adjusted R square? R square and adjusted R square values are used for model validation in case of linear regression. R square indicates the variation of all the independent variables on the dependent variable. i.e. it considers all the independent variable to explain the variation. In the case of Adjusted R squared, it considers only significant variables(P values less than 0.05) to indicate the percentage of variation in the model. Thus Adjusted R2 is always lesser then R2. 3. What is stratify in Train_test_split? Stratification means that the train_test_split method returns training and test subsets that have the same proportions of class labels as the input dataset. So if my input data has 60% 0's and 40% 1's as my class label, then my train and test dataset will also have the similar proportions. 4. What is Backpropagation in Artificial Neuron Network? Backpropagation is the method of fine-tuning the weights of a neural network based on the error rate obtained in the previous epoch (i.e., iteration). Proper tuning of the weights allows you to reduce error rates and make the model reliable by increasing its generalization. ENJOY LEARNING 👍👍

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https://t.me/CryptoSignalsLeaked/7791 We are giving away 100$ in cash, two memberships for R&A signals for 1 month, and it's allll for free!🔥 All you Don't miss out! It's free for everyone!

77 661

Thinking_in_Pandas_How_to_Use_the_Python_Data_Analysis_Library_the.pdf2.31 MB

77 661

How is kNN different from k-means clustering? kNN, or k-nearest neighbors is a classification algorithm, where the k is an integer describing the number of neighboring data points that influence the classification of a given observation. K-means is a clustering algorithm, where the k is an integer describing the number of clusters to be created from the given data. Both accomplish different tasks.