Unsupervised learning
In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns)
You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you
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In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns)
You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you
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Algorithm Name
K-means clustering
Description
Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans)
Type
Clustering
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K-means clustering
Description
Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans)
Type
Clustering
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Algorithm Name
Gaussian mixture model
Description
A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters)
Type
Clustering
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Gaussian mixture model
Description
A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters)
Type
Clustering
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Algorithm Name
Hierarchical clustering
Description
Splits clusters along a hierarchical tree to form a classification system.
Can be used for Cluster loyalty-card customer
Type
Clustering
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Hierarchical clustering
Description
Splits clusters along a hierarchical tree to form a classification system.
Can be used for Cluster loyalty-card customer
Type
Clustering
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Algorithm Name
Recommender system
Description
Help to define the relevant data for making a recommendation.
Type
Clustering
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Recommender system
Description
Help to define the relevant data for making a recommendation.
Type
Clustering
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Algorithm Name
PCA/T-SNE
Description
Mostly used to decrease the dimensionality of the data. The algorithms reduce the number of features to 3 or 4 vectors with the highest variances
Type
Dimension Reduction
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PCA/T-SNE
Description
Mostly used to decrease the dimensionality of the data. The algorithms reduce the number of features to 3 or 4 vectors with the highest variances
Type
Dimension Reduction
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Concatenate DataFrames in Python
https://www.pythonforbeginners.com/basics/concatenate-dataframes-in-python
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https://www.pythonforbeginners.com/basics/concatenate-dataframes-in-python
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Telepathy.
An OSINT toolkit for investigating Telegram chats. Developed by Jordan Wildon.
https://github.com/jordanwildon/Telepathy
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An OSINT toolkit for investigating Telegram chats. Developed by Jordan Wildon.
$ pip3 install telepathyhttps://github.com/jordanwildon/Telepathy
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طبق گفته گویدو ون راسم، خالق پایتون، نسخهی 3.11 قراره به صورت لایو، دو روز دیگه یعنی دوشنبه، ساعت ۱۷ به وقت UTC (یعنی ساعت 20:30 به وقت تهران) ریلیس بشه 😍
https://twitter.com/gvanrossum/status/1583561788204806144?t=e5oNBr7PJc27y0PnSo9Xqw&s=19
این لینک یوتوبش 😁
https://youtu.be/PGZPSWZSkJI
https://twitter.com/gvanrossum/status/1583561788204806144?t=e5oNBr7PJc27y0PnSo9Xqw&s=19
این لینک یوتوبش 😁
https://youtu.be/PGZPSWZSkJI
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Do you want to watch how we release Python 3.11 live? 🐍🎉 Join us in the 3.11 release party organised with the good people of @PythonDiscord at 17:00 UTC+0! 📆 We will talk about some of the new cool features and a sneak peek into what's coming in 3.12 👀 https://youtu.be/PGZPSWZSkJI
🔗 Pablo Galindo Salgado (@pyblogsal)
تا ریلیس شدن پایتون ۳.۱۱ ساعاتی بیش نمانده است =))
🔗 Pablo Galindo Salgado (@pyblogsal)
تا ریلیس شدن پایتون ۳.۱۱ ساعاتی بیش نمانده است =))
YouTube
Python 3.11 Release
Timestamps
00:00 - Introduction
24:30 - Brandt Bucher, Specializing Adaptive Interpreter
50:40 - Mark Shannon, Other Speedups
1:07:42 - Irit Katriel, Exception Improvements and Features
1:42:13 - Pablo Galindo, Better Tracebacks
1:58:46 - Pablo Galindo, tomllib…
00:00 - Introduction
24:30 - Brandt Bucher, Specializing Adaptive Interpreter
50:40 - Mark Shannon, Other Speedups
1:07:42 - Irit Katriel, Exception Improvements and Features
1:42:13 - Pablo Galindo, Better Tracebacks
1:58:46 - Pablo Galindo, tomllib…
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