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🚀 This is a repository to learn and gain more knowledge about ML Engineering and Data Science topics. :octocat:

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Elkinmt19/ml-engineering-dojo

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THE ML ENGINEERING DOJO

This is a repository created to acquire and develop all the necessary skills to become a Machine Learning Engineer. This project has a lot of projects related to data analysis with Python, using tools such as NumPy, Pandas and Matplotlib. It also has machine learning projects using technologies such as Sklearn, TensorFlow, Pytorch, MXNet and AWS SageMaker.

SOFTWARE DEPENDENCIES 💻

PYTHON DEPENDENCIES

  • Python
    Python is a programming language that lets you work quickly and integrate systems more effectively.

  • Numpy
    Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python.

  • Pandas
    Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,built on top of the Python programming language.

  • Matplotlib
    Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

  • Scikit-learn
    Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection, model evaluation, and many other utilities.

  • TensorFlow
    TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

AUTHOR

Elkin Javier Guerra Galeano

Data scientist at Pragma S.A., excited for integrating Software and Hardware systems.
He is curious about machine learning and artificial intelligence.
He has skills with problem-solving for real-life applications. He is passionate about building knowledge from a theory-practice approach.

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