NumPy: It is a Python library used for working with arrays. It also has functions for working in the domain of linear algebra, Fourier transform, and matrices.
Scikit-learn (Sklearn): It is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction via a consistent interface in Python.
Pandas: It is mainly used for data analysis. Pandas allows importing data from various file formats such as comma-separated-values, JSON, SQL database tables or queries, and Microsoft Excel.
Matplotlib: It is a cross-platform, data visualization and graphical plotting library for Python and its numerical extension NumPy. As such, it offers a viable open-source alternative to MATLAB. Developers can also use matplotlib's APIs (Application Programming Interfaces) to embed plots in GUI applications.
Jupyter: The Jupyter Notebook is an open-source web application thatallowsdata scientists to create and share documents that integrate live code,equations, computational output, visualizations, and other multimediaresources, along with explanatory text in a single document.
Tensorflow
PyTorch
Keras
SKLearn
numpy
matplotlib
pandas
Concat
Stochastic gradient descent
multi-layer perceptron
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