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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
Больше📈 Аналитический обзор Telegram-канала Data Science & Machine Learning
Канал Data Science & Machine Learning (@datasciencefun) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 75 899 подписчиков, занимая 2 103 место в категории Образование и 4 204 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 75 899 подписчиков.
Согласно последним данным от 23 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 731, а за последние 24 часа — 33, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.95%. В первые 24 часа после публикации контент обычно набирает 0.86% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 239 просмотров. В течение первых суток публикация набирает 650 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, accuracy, distribution, panda, dataset.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”
Благодаря высокой частоте обновлений (последние данные получены 24 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
75 899
Подписчики
+3324 часа
+587 дней
+73130 день
Архив постов
You will prefer YouTube videos in which language?
Perfect resources for beginners
https://www.kaggle.com/kanncaa1/data-sciencetutorial-for-beginners
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
Top Machine Learning Case studies
https://nuggetsnetwork.com/blog/Machine-Learning-Case-Studies.html
🔰Data Science [All Courses] 🔰
🌀Source : Udacity
🌀Size : 54.05 GB
🔗Link:
https://mega.nz/#F!qrpxSIRD!PClG5ZMHdd5FroIFTT_Z5Q
💢 Share and Support Us 💢
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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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
Intershala Machine learning
https://drive.google.com/folderview?id=1tZFMiQ-ZD9rZ4epFSd4lVCCuwqhQt0_9
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.
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.
