7 Python libraries every machine learning engineer should know.

(A thread) 👇🧵

1. NumPy

NumPy is a fundamental library for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices of numerical data, as well as functions to perform operations on these data structures.

🔗 numpy.org/

2. Pandas

Pandas is a library for data manipulation and analysis. It provides functions and data structures for efficiently working with large datasets, including support for handling missing data, time series analysis, and merging and joining data.

🔗 pandas.pydata.org/

3. scikit-learn

scikit-learn is a library for machine learning in Python. It provides a wide range of algorithms for classification, regression, clustering, and model selection, as well as tools for evaluating the performance of these models.

🔗 scikit-learn.org/stable/

4. PyTorch

PyTorch is an open-source machine-learning library developed by Facebook. It is a popular choice for deep learning and provides support for dynamic computation graphs, which allow for more flexible and efficient model design.

🔗 pytorch.org/

5. TensorFlow

TensorFlow is an open-source machine-learning library developed by Google. It provides a flexible and efficient platform for building, training, and deploying machine learning models, including support for deep learning.

🔗 www.tensorflow.org/

6. Keras

Keras is a high-level library for building and training neural networks. It is built on top of TensorFlow and provides a simple and intuitive interface for defining and training models. It is well-suited for quick prototyping.

🔗 keras.io/

6. Matplotlib:

Matplotlib is a library for creating visualizations in Python. It provides a wide range of tools for creating plots, charts, and other visualizations of data.

🔗 matplotlib.org/

7. Seaborn

Seaborn is a library for creating statistical graphics in Python. It is built on top of Matplotlib and provides a higher-level interface for creating visualizations, including support for more complex plots and advanced aesthetics.

🔗 seaborn.pydata.org/

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