Top underrated libraries in machine learning
These libraries will definitely save your time
In the fields of data science and machine learning, Python is one of the most frequently used programming languages. Because of its shallow learning curve and plenty of libraries and packages, it is highly well-liked among developers. A collection of modules called Python libraries helps developers create software more quickly.
Lazypredict
One of the challenging jobs is choosing the ideal model for your machine learning problem statement. One of the greatest Python libraries for semi-automating machine learning tasks is this one. It creates a large number of fundamental models with little to no coding and aids in understanding which models perform better without parameter adjusting. The library simultaneously trains more than 50 models and displays the output from each one. aiding in the comparison of the best model for your dataset.
Streamlit
The same way you write Python code, you can employ Streamlit to develop web applications. Streamlit makes it easy to work on the continuous cycle of coding and viewing results on the web app. Without any prior understanding of web programming, you may quickly create websites with the aid of streamlit.
KNIME
Anyone can understand data and develop analytical workflows thanks to KNIME, a low-code data science and data preparation tool. It makes data analysis easier and requires less coding. It facilitates the production of statistics, aggregates, data cleaning, feature extraction, and feature selection.
Pycaret
It is a complete machine learning and model management application that increases productivity and exponentially shortens the trial cycle. It enables users to conduct end-to-end investigations rapidly and efficiently. PyCaret is an alternative low-code library to the other open-source machine learning libraries that may be used to carry out complicated machine learning tasks with just a few lines of code. It’s straightforward and convenient to use PyCaret.
Terality
Users can quickly transform all of their data with Terality, which has the same syntax as pandas and can handle terabytes of data. You may use Terality to create code that reads, processes, and outputs your datasets. No matter the size of your datasets, the Terality infrastructure autoscales in a matter of seconds and parallelizes your data processing pipelines without any configuration on your behalf.
OpenCV
An important open-source library for computer vision, machine learning, and image processing is called OpenCV. It was developed to speed up the incorporation of machine perception into consumer goods and to offer a standardised infrastructure for computer vision applications.
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