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 Expert India4 204 Education2 103...

📈 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 75 899 subscribers, ranking 2 103 in the Education category and 4 204 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 75 899 subscribers.

According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 731 over the last 30 days and by 33 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.95%. Within the first 24 hours after publication, content typically collects 0.86% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 239 views. Within the first day, a publication typically gains 650 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 24 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.

75 899

Subscribers

+3324 hours

+587 days

+73130 days

2 239

Post views

~ 65024 hours

~ 85748 hours

2.95%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

75 898

Image recognition is an example of?

Anonymous voting

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You will prefer YouTube videos in which language?

Anonymous voting

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Rules of Machine Learning.pdf4.49 KB

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Perfect resources for beginners https://www.kaggle.com/kanncaa1/data-sciencetutorial-for-beginners

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For free machine learning, data science, ethical hacking and general programming courses join @bigdataspecialist channel. He also has discord server where you can ask anything about data science/machine learning and programing in general. https://discord.gg/f4sXD37H9q

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Git Cheatsheet

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Top Machine Learning Case studies https://nuggetsnetwork.com/blog/Machine-Learning-Case-Studies.html

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🔰Data Science [All Courses] 🔰 🌀Source : Udacity 🌀Size : 54.05 GB 🔗Link: https://mega.nz/#F!qrpxSIRD!PClG5ZMHdd5FroIFTT_Z5Q 💢 Share and Support Us 💢

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https://github.com/datastacktv/data-engineer-roadmap

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https://towardsdatascience.com/my-journey-into-data-science-39e9bbbbf452

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https://towardsdatascience.com/you-should-master-data-analytics-first-before-becoming-a-data-scientist-5dbceaea9d3d

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Amazon is hiring Position: Data Science Intern 👉 Apply: https://www.amazon.jobs/en/jobs/1008217/data-scientist-intern 👍 All the best.

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Open in app Responses To respond to this story, get the free Medium app. Open in app There are currently no responses for this story. Be the first to respond. What is it to be like Data Scientist (From a guy who has been one and now hires them!) Harsh Gupta 13 hours ago·3 min read I have worked as a Data Scientist for 4+ years and now manage data science teams. I have also worked with data science teams at multiple Fortune 500 companies. Here are my best observations about what it is like to be a data scientist as of Feb 2021. Best Case You work on very exciting problems in the realm of data science/ AI as well as for your business. You are publishing, you are thinking about new solutions all the time, and you are using your creative juices to the fullest. You are working with very interesting people inside and outside of your organization. Your team has visibility to senior leadership. You also have access to subject matter experts in your company, in AI research labs, at vendors who are thou

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SQL for Data Science

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Intershala Machine learning https://drive.google.com/folderview?id=1tZFMiQ-ZD9rZ4epFSd4lVCCuwqhQt0_9

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What do you want to learn?

Anonymous voting

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Logistic regression fits a logistic model to data and makes predictions about the probability of an event (between 0 and 1). Naive Bayes uses Bayes Theorem to model the conditional relationship of each attribute to the class variable. The k-Nearest Neighbor (kNN) method makes predictions by locating similar cases to a given data instance (using a similarity function) and returning the average or majority of the most similar data instances. The kNN algorithm can be used for classification or regression. Classification and Regression Trees (CART) are constructed from a dataset by making splits that best separate the data for the classes or predictions being made. The CART algorithm can be used for classification or regression. Support Vector Machines (SVM) are a method that uses points in a transformed problem space that best separate classes into two groups. Classification for multiple classes is supported by a one-vs-all method. SVM also supports regression by modeling the function with a minimum amount of allowable error.

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Three different learning styles in machine learning algorithms: 1. Supervised Learning Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time. A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data. Example problems are classification and regression. Example algorithms include: Logistic Regression and the Back Propagation Neural Network. 2. Unsupervised Learning Input data is not labeled and does not have a known result. A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity. Example problems are clustering, dimensionality reduction and association rule learning. Example algorithms include: the Apriori algorithm and K-Means. 3. Semi-Supervised Learning Input data is a mixture of labeled and unlabelled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions. Example problems are classification and regression. Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

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