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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 821Education2 017...

📈 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 77 621 subscribers, ranking 2 017 in the Education category and 3 821 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 77 621 subscribers.

According to the latest data from 13 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -142 over the last 30 days and by -20 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.37%. 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 1 843 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 14 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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77 621

Subscribers

-2024 hours

-957 days

-14230 days

1 843

Post views

~ 78124 hours

~ 1 05048 hours

2.37%

Engagement rate

~ 2

Posts per day

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Posts Archive

77 621

Quiz Explaination Supervised Learning: All data is labeled and the algorithms learn to predict the output from the input data Unsupervised Learning: All data is unlabeled and the algorithms learn to inherent structure from the input data. Semi-supervised Learning: Some data is labeled but most of it is unlabeled and a mixture of supervised and unsupervised techniques can be used to solve problem. Unsupervised learning problems can be further grouped into clustering and association problems. Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy A also tend to buy B.

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Which of the following is not a type of unsupervised Learning?

Anonymous voting

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In which technique, data is unlabeled and the algorithms learn to inherent structure from the input data?

Anonymous voting

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Master_Machine_Learning_Algorithms_Discover_how_they_work_by_Jason.pdf1.09 MB

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machine-learning-interview-questions.pdf2.11 KB

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What are the main parameters of the random forest model? max_depth: Longest Path between root node and the leaf min_sample_split: The minimum number of observations needed to split a given node max_leaf_nodes: Conditions the splitting of the tree and hence, limits the growth of the trees min_samples_leaf: minimum number of samples in the leaf node n_estimators: Number of trees max_sample: Fraction of original dataset given to any individual tree in the given model max_features: Limits the maximum number of features provided to trees in random forest model

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🎓 Introduction to Deep Learning (by MIT) 🎓 This is one of the top high-quality courses to learn the foundational knowledge of deep learning. All lectures have been uploaded. 100% Free! https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

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Pandas in 8 Pages.pdf8.28 KB

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What are the main parameters in the gradient boosting model? There are many parameters, but below are a few key defaults. learning_rate=0.1 (shrinkage). n_estimators=100 (number of trees). max_depth=3. min_samples_split=2. min_samples_leaf=1. subsample=1.0.

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Which of the following Python Library can be exclusively used to plot graphs?

Anonymous voting

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How does L2 regularization look like in a linear model? L2 regularization adds a penalty term to our cost function which is equal to the sum of squares of models coefficients multiplied by a lambda hyperparameter. This technique makes sure that the coefficients are close to zero and is widely used in cases when we have a lot of features that might correlate with each other.

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Which regularization techniques do you know? There are mainly two types of regularization, L1 Regularization (Lasso regularization) - Adds the sum of absolute values of the coefficients to the cost function. L2 Regularization (Ridge regularization) - Adds the sum of squares of coefficients to the cost function Here, Lambda determines the amount of regularization.

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Machine Learning for Everyone in some words https://vas3k.com/blog/machine_learning/

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Everything you need to know about TensorFlow 2.0 Keras-APIs, SavedModels, TensorBoard, Keras-Tuner and more. https://hackernoon.com/everything-you-need-to-know-about-tensorflow-2-0-b0856960c074?

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Today is the last day to get exclusive 75% discount by using coupon code JULY75

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What happens to our linear regression model if we have three columns in our data: x, y, z — and z is a sum of x and y? We would not be able to perform the regression. Because z is linearly dependent on x and y so when performing the regression would be a singular (not invertible) matrix.

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What’s the difference between random forest and gradient boosting? Random Forests builds each tree independently while Gradient Boosting builds one tree at a time. Random Forests combine results at the end of the process (by averaging or "majority rules") while Gradient Boosting combines results along the way.

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Free Live session on Deep Learning technology used by Amazon Go 👇👇 https://bit.ly/3e1BCLs Only for Indian Users

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Which algorithm builds one tree at a time?

Anonymous voting

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What do we do with categorical variables? Categorical variables must be encoded before they can be used as features to train a machine learning model. There are various encoding techniques, including: One-hot encoding Label encoding Ordinal encoding Target encoding