Chapter 19 Favorite machine learning frameworks

Favorite 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”

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.
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
- MOOC²