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machine-learning in Python

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35 reviews
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machine-learning in Python Reviews

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Savannah L.
SL
Savannah L.
Post-Baccalaureate IRTA at National Institute of Mental Health (NIMH)
07/30/2018
Validated Reviewer
Review source: G2 invite
Incentivized Review

Machine learning in python can be used by even the most technologically challenged!

There are so many well-documented, common-sense, easily implementable python scripts and packages for machine learning. Scikit learn has some amazing tutorials, for concept learning, function learning or “predictive modeling”, and clustering and finding predictive patterns. With the language of python itself, it is easy to understand how to utilize the Kmeans algorithm, and implement aspects of machine learning with your own data.
Verified User in Computer Software
GC
Verified User in Computer Software
07/30/2018
Validated Reviewer
Review source: G2 invite
Incentivized Review

Python - The easiest way to get your hands dirty in machine learning

The ease of implementation that python libraries offer and available documentation.
Verified User in Medical Practice
GM
Verified User in Medical Practice
07/13/2018
Validated Reviewer
Verified Current User
Review source: G2 invite
Incentivized Review

Easy to learn, many resource = efficient!

Machine learning with Python is very much easy to set up. Once you have download Python, assuming if you download with Spyder and Anaconda, everything will be pre-packaged. For people with amataeur coding knowledge like me, whenever I hit a brick wall, I’m able to go online and find solutions.

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What is machine-learning in Python?

The repository "machine-learning" by jeff1evesque on GitHub provides a comprehensive solution for implementing machine learning algorithms in Python. This project offers a robust framework designed to facilitate the development of machine learning models, emphasizing ease of use and scalability. It likely includes various utilities and pre-built components to assist users in creating and training models, handling data preprocessing, evaluation, and optimization tasks. As an open-source project, it encourages collaboration and contributions from developers and researchers interested in enhancing or extending its functionality. You can access the repository and its resources at https://github.com/jeff1evesque/machine-learning.

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