27.18 Favourite machine learning frameworks

Favourite machine learning frameworks are shown in the graph below

The last Qualification which is cut off in the legend in the plot above reads “Some college/university study without earning a bachelor’s degree”

Links to ML frameworks

  • Scikit-learn
    • Machine Learning in Python
    • Open source, commercially usable - BSD license
  • TensorFlow
    • An end-to-end open source machine learning platform
  • Keras
    • Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
  • RandomForest
    • A random forest classifier
  • Xgboost
    • XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
  • PyTorch
    • An open source machine learning framework that accelerates the path from research prototyping to production deployment.
    • On 30.01.2020 OpenAI announced OpenAI → PyTorch
  • LightGBM
    • LightGBM is a gradient boosting framework that uses tree based learning algorithms.
  • Caret
    • The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models.
    • For the programming language R
  • Fast.ai
    • Making neural nets uncool again
    • Blogs
    • MOOC25

  1. Massive Open Online Courses↩︎