Introduction to Machine Learning with Python: A Guide for Data Scientists 1st Edition
by Andreas C. Müller
https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413

University of Michigan - Ann Arbor

Python is widely used for machine learning various reasons.
- It is relatively easy to learn.
- The data handling capacity is better than most other languages.
- The community is large, as well as support for other platforms.
- Its’s Open Source.
- It supports developers with frameworks and libraries such as TensorFlow


Machine Learning with Python

Python Libraries are chosen mostly based on their scalability and the area of tasks the program needs to specialize in. Here are some of common libraries that are in use:

- Keras - one of the most popular and open-source neural network libraries for Python

- PyTorch - Developed by Facebook, support for C++ with its C++ interface

- Scikit-learn - easy integration with different ML programming libraries like NumPy and Pandas

- NLTK(Natural Language Toolkit) - Python library for working with natural language processing

- Spark MLlib - Developed by Apache, enables easy scaling of your computations

- Theano - a robust library for carrying out scientific calculations on a large-scale

-TensorFlow - An open source library developed by Google for training neural networks.

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