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Resources for getting started with ML analysis

The purpose of this topic is to populate relevant resources on AI/ML. Disclaimer: This an initial collation of material and not a specific endorsement.

  1. Deep learning specialization (Online Course)
    This five-course specialization will help you understand Deep Learning fundamentals, apply them, and build a career in AI
    Link: https://www.deeplearning.ai/deep-learning-specialization/#:~:text=How%20much%20does%20the%20course,can%20enroll%20in%20individual%20courses.

  2. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (Textbook)
    This practical book is focused on teaching programmers how to implement machine learning programs using both the scikit-learn and TensorFlow frameworks.
    Link: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

  1. Reddit ML forum (Discussion Forum)
    Discussion forum for various topics in ML
    Link: https://www.reddit.com/r/MachineLearning/

  2. Machine Learning in R for beginners (Tutorial)
    A short tutorial that introduces the learners to implementing ML using R
    Link: https://www.datacamp.com/community/tutorials/machine-learning-in-r (2018)
    Source: Talevi et al. 2020

  1. Stanford ML Tips and Tricks (Cheatsheet)
    A cheatsheet/reference for important concepts and methods in ML
    Link: https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks
    Source: Talevi et al. 2020

  2. Kaggle Datasets (datasets)
    A dataset repository to build ML models
    Link: https://www.kaggle.com/datasets
    Source: Talevi et al. 2020

  1. Weka (Open source ML tool)
    An open-source ML tool with a user interface.
    Link: https://www.cs.waikato.ac.nz/ml/weka/
    Source: Talevi et al. 2020

  2. Google Colaboratory (Free cloud-based ML tool)
    A free cloud-based ML tool that provides an python jupyter notebook with minimum/setup necessary.
    Link: https://colab.research.google.com/notebooks/welcome.ipynb
    Source: Talevi et al. 2020

  1. Caret R package (Open source ML tool)
    An open-source R package for ML analysis.
    Link: https://cran.r-project.org/web/packages/caret/caret.pdf
    Source: Talevi et al. 2020