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machine-learning-algorithms

Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.

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nni
Learn-Data-Science-For-Free

This repositary is a combination of different resources lying scattered all over the internet. The reason for making such an repositary is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. For Constant Updates Follow me in Twitter.

  • Updated Apr 2, 2021
beckernick
beckernick commented Jul 7, 2021

For feature engineering tasks, I'd like to be able to determine whether a datetime is the beginning or end of a year, like I can in pandas.

import pandas as pds = pd.Series(["2021-02-27", "2020-03-31"], dtype="datetime64[ms]")
s.dt.is_year_end
0    False
1    False
dtype: bool
import pandas as pds = pd.Series(["2021-01-01", "2020-04-01"], dtype="datet
igel
wphicks
wphicks commented Feb 8, 2021

Report needed documentation

Report needed documentation
While the estimator guide offers a great breakdown of how to use many of the tools in api_context_managers.py, it would be helpful to have information right in the docstring during development to more easily understand what is actually going on in each of the provided functions/classes/methods. This is particularly important for

adocherty
adocherty commented Nov 27, 2019

Description

Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.

This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/

ssimontacchi
ssimontacchi commented Jun 20, 2020

Hi, Thanks for the awesome library!

So I am running a Kmeans on lots of different datasets, which all have roughly four shapes, so I initialize with those shapes and it works well, except for just a few times. There are a few datasets that look different enough that I end up with empty clusters and the algorithm just hangs ("Resumed because of empty cluster" again and again).

I conceptually

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