کتابخانه مهندسی کامپیوتر و پایتون
@alloadv تبلیغات ادمین : @maryam3771
Show more📈 Analytical overview of Telegram channel کتابخانه مهندسی کامپیوتر و پایتون
Channel کتابخانه مهندسی کامپیوتر و پایتون (@programmers_street) in the Farsi language segment is an active participant. Currently, the community unites 31 361 subscribers, ranking 4 355 in the Technologies & Applications category and 10 849 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 31 361 subscribers.
According to the latest data from 24 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 2 267 over the last 30 days and by 106 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.43%. Within the first 24 hours after publication, content typically collects 1.46% reactions from the total number of subscribers.
- Post reach: On average, each post receives 762 views. Within the first day, a publication typically gains 459 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
- Thematic interests: Content is focused on key topics such as مصنوعی, دنیا, ابزار, آموزش, پایتون.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“@alloadv تبلیغات
ادمین : @maryam3771”
Thanks to the high frequency of updates (latest data received on 25 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 Technologies & Applications category.
Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:1. Supervised Learning: The algorithm is trained on a labeled datasets, learning to map input to output. For example, it can predict housing prices based on features like size and location. 2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing. 3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications. 📖 Key concepts include: - Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training. - Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance. - Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns. - Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks. In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.
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