Learning resources
Data Science

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  • MACHINE LEARNING. THEORY

    1. Peter Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data.
    2. Yoshua Bengio et al, Deep Learning.

  • PYTHON LANGUAGE

    1. Mark Lutz. Learning Python. Volume 1, 2.

  • DATA ANALYSIS IN PYTHON. PANDAS AND NUMPY LIBRARIES

    1. Michael Haidt. Studying Pandas
    2. Wes McKinney. Python for Data Analysis
    3. J. Vander Plas. Python for Complex Tasks: Data Science and Machine Learning

  • MACHINE LEARNING IN PYTHON, SCIKIT-LEARN LIBRARY

    1. Andreas Müller, Sarah Guido. Introduction to Machine Learning with Python.
    2. Sebastian Raschka. Python Machine Learning.

  • DEEP LEARNING. KERAS, TENSORFLOW, AND PYTORCH LIBRARIES

    1. François Chollet. Deep Learning with Python.
    2. Antonio Gulli, Sujit Pal. Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python.
    3. Aurélien Géron. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow.
    4. Bharath Ramsundar, Reza Bosagh Zadeh. TensorFlow for Deep Learning.
    5. Brian McMahan, Delip Rao. Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning.

  • COMPUTER VISION

    1. Jan Erik Solem. Programming Computer Vision with Python.

  • DOCKER CONTAINERS

    1. Adrian Mowat. Using Docker.

  • ONLINE COURSES

    1. Coursera | Machine Learning for Data Analysis.
    2. Coursera | Deep Learning Specialization.

  • A PLATFORM FOR COMPETITIONS AMONG DATA SCIENTISTS

    1. www.kaggle.com