While Python’s popularity as the go-to programming language for data science has grown immensely in recent years, so has the number of great libraries and frameworks available to help you create powerful data analysis apps quickly and easily. No longer do you have to learn how to code your own machine learning algorithms or write code from scratch—the following six libraries and frameworks take care of all the heavy lifting, making it easy to build complex apps without having to reinvent the wheel yourself. Here are six top Python libraries and frameworks that will streamline your machine learning application development.

1) PyTorch

PyTorch is an open source machine learning library. PyTorch is used by Facebook AI Research (FAIR) to build its neural network models, and currently it's being used in production in many companies. PyTorch also has an active community with thousands of contributors. When you develop a deep learning model using PyTorch, you write your code using TorchScript, which runs seamlessly on CPU or GPU.

2) Scikit-Learn

It provides a powerful, high-level API to implement various machine learning and data mining algorithms. It is written in Python and runs on top of NumPy, SciPy, and matplotlib. It supports classification, regression and feature hashing as supervised learning tasks. It also supports unsupervised learning such as clustering and dimensionality reduction.

3) Keras

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras has no dependencies other than numpy, and can run on GPU and CPU machines seamlessly. Hire Python programmers at affordable rates.

4) TensorFlow

TensorFlow is a powerful machine learning framework originally developed by researchers and engineers working on Google’s deep learning project. It’s an open source software library that can be used to implement a wide variety of popular machine learning models and algorithms, including neural networks, regression analysis, deep learning, and so on. It was originally released in November 2015.

5) MXNet

Developed by Amazon and generally praised as one of top frameworks for machine learning. MXNet is a deep learning framework that allows users to write complex neural networks with a concise, readable syntax. It can be used in conjunction with NumPy arrays and scalable parallelism if deployed on GPUs. The core code includes support for directed graphs, GPU acceleration, convolutional networks, recurrent networks and dynamic computation graphs.

6) XGBoost

XGBoost is a general purpose gradient boosting library designed to be efficient, flexible and portable. It's currently used in many leading machine learning competition frameworks, including scikit-learn, Spark MLlib and TensorFlow. XGBoost provides programmers with a highly optimized toolkit that can be used to quickly develop both accurate models and fast learners for various classification tasks.

Final Words

Be sure to read about each of these libraries, as they all bring something valuable to the machine learning table. Check out what niche your company’s product or service can fill and how it complements or enhances any one of these tools. Remember, to stay on track with your marketing strategy, you’ll need to show why you’re better than everyone else; one way to do that is to establish expertise in a given topic through thorough research. Get in touch with a machine learning company for great results.