In my book, there are plenty of libraries that can enable us to use ML techniques but five of them are most important.

۱-TensorFlow

TensorFlow is a high-level neural network library that helps you program your network architectures while avoiding the low-level details. 

۲-scikit-learn: It’s built on top of the popular NumPy, SciPy, and matplotlib libraries, so it’ll have a familiar feel to it for the many people that already use these libraries

۳-Theano:

good for Neural networks and deep learning

۴-NumPy

It provides an abundance of useful features for operations on n-arrays and matrices in Python. The library provides vectorization of mathematical operations on the NumPy array type, which ameliorates performance and accordingly speeds up the execution.

۵-SciPy

SciPy is a library of software for engineering and science. Again you need to understand the difference between SciPy Stack and SciPy Library. SciPy contains modules for linear algebra, optimization, integration, and statistics. The main functionality of SciPy library is built upon NumPy, and its arrays thus make substantial use of NumPy.