Top 5 machine learning library in Python

Scikit-Learn

Scikit-learn is open source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN. It is well-designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The package is built on the top of SciPy and makes heavy use of its math operations.
Check out official website: SciKit-Learn

Deep Learning Libraries

one of the most prominent libraries for Python in the feild of deep learning is Keras, which can function either on top of TensorFlow or Theano.

TensorFlow

An open-source software library for Machine Intelligence. TensorFlow was developed by Google Brain team and they  made it open source on November 9, 2015. It is a successor of DistBelief Net which a Machine Learning system, based on Neural Networks. This is very efficient for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Check out official website: TensorFlow

Theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is similar to NumPy, along with math operations and expressions. Theano was developed by the Machine Learning group of Université de Montréal. The library also optimizes the use of GPU and CPU and make the performance of data-intensive computation even faster. Actually, it serves as the building blocks for neural networks whereas NumPy serves the building blocks for scientific computing.
Check out official website: Theano

Keras

Keras is a prominent open source library written in Python for building Neural Networks. It is capable of running on top of MXNet, Deeplearning4j, Tensorflow, Microsoft Cognitive Toolkit(CNTK) or Theano. The library contains numerous implementations of commonly used neural network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier.
Check out official website: Keras

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Lasgane

Lightweight library to build and train neural networks in Theano. It supports Convolutional Neural Networks (CNNs), recurrent networks including Long Short-Term Memory (LSTM). It provides transparent support of CPUs and GPUs due to Theano’s expression compiler. You can use this if you want the flexibility of Theano but don’t want to always write neural network layers from scratch.
Check out official website: Lasagne

MXNet

MXNet is a flexible and efficient library for Deep Learning. The incredible thing is it supports over 7 different language bindings including C++, Python, R, Javascript, and even Matlab. If you want the speed of training over multiple CPUs or GPUs, MXNet is your best friend as it supports distributed computing.
Check out official website: MXNet

Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Google’s DeepDream is based on Caffe Framework.  Caffe isn’t a Python library but it does provide bindings into the Python programming language. Actually, it is a BSD-licensed C++ library which provides a python interface.
Check out official website: Caffe