For those who are relatively new to this concept, machine learning is a subset of artificial intelligence (AI) that provides computer systems with the ability to perform an array of tasks without being explicitly programmed. ML enables computers to automatically learn from experience and patterns, thus leading to more efficient decisions in the future.

Below is an illustration of the typical ML process:

How is machine learning used today? Machine learning has several applications in the modern world including precision medicine, speech recognition, image recognition, financial services, prediction, extraction, regression, just to name a few. A perfect example of Machine learning application in medicine is the recent study by TheAppsolutions and Google experts about nanopore  DNA sequencers on Google Cloud.

That said, let’s highlight some excellent development tools for machine learning one by one.


The Google Brain Team released TensorFlow back in 2015. It is a blend between network specification libraries (Blocks and Lasagne) and symbolic computation libraries like Theano. TensorFlow is primarily used for expressing algorithms that entail many Tensor (an N-dimensional matrix that corresponds to your data) operations. It allows data scientists to create data flow graphs or a sequence of processing nodes. Every node in the graph signifies a mathematical operation, while each connection between the multiple nodes is a multidimensional data array. Python gives a convenient front-end Application Processing Interface for developing apps with the framework while executing those applications in high-performance C++.

TensorFlow allows data scientists to train and run neural networks for image recognition, handwritten digit classification, word embeddings, natural language processing, simulations, recurrent neural networks, partial differential equations (PDE), scaled production predictions, and sequence-to-sequence models for machine translation.


Scikit-Learn is an ML library for Python designed to interoperate with scientific and numerical libraries such as SciPy and NumPy. It is primarily used when bringing ML into a production system.

It offers a range of supervised and unsupervised learning algorithms via a consistent interface in Python. Scikit-Learn focuses on documentation, code quality, performance, and ease of use. Now, given that Scikit-Learn is all about modeling data, it does not shine in manipulating, summarizing, and loading of data. Check out Scikit-Learn tutorials for a deeper understanding of this library.


PyTorch is a relatively new Python deep learning library. Backed by Facebook AI Research Team (FAIR), PyTorch can manage dynamic computation graphs something that Theano, TensorFlow, and other derivatives cannot.

It has a user-friendly API and easily integrates with the Python data science stack. It allows users to develop computational graphs on the go, and better yet, alter them during run-time. This ability is particularly useful when the developer does not know the exact amount of memory needed for creating a neural network. More advantages of PyTorch include custom data loaders, simplified preprocessors, and multi GPU support.

Feel free to refer to PyTorch tutorials to learn more.


Keras is a highly effective neural networking API written in Python. It is a user-friendly ML library used for creating neural networks that run on top of TensorFlow, Cognitive Toolkit, and Theano. Keras was developed to enable quicker experimentation.

Keras is ideal if for developers who are keen on finding a Python library that runs smoothly on CPU and GPU. It supports convolutional and recurrent networks or combinations of these and allows for quick and straightforward prototyping.

Keras users can decide to execute the models they create on symbolic graphs of Theano and TensorFlow. Working with Keras is easier if you are familiar with ML in the Lua language. Check out some Keras example models.


This Python library allows data scientists to evaluate, define, and optimize mathematical expressions that entail multi-dimensional arrays. Theano is one of the widely used deep learning libraries and is quite similar to NumPy.

Theano optimizes the use of GPU, CPU and boosts the performance of data-intensive computations. Theano’s code takes advantage of the workings of a computer profiler. Refer to the Theano tutorial for more details.


Developed by Berkeley AI Research (BAIR) and other community contributors, Caffe is a deep learning framework that comes with expressive architecture that allows for innovation and application. With Caffe, both optimization and models are defined by configuration without the need for hard-coding. Users can easily switch between GPU and CPU by setting one flag to train on a specific GPU machine and later deploy to mobile devices or commodity clusters.

Caffe is advantageous since it offers extensible code and is fast.


Chainer is an ML framework for neural systems designed to run in procedures. It allows developers to change systems during runtime, thus letting them implement control stream articulations. It sustains both multi-GPU and CUDA calculations. Chainer is primarily used for machine interpretation, discourse acknowledgment, assumption investigation, etc., using CNNs and RNNs.

Microsoft Cognitive Toolkit

Microsoft CNTK is an open-source ML framework that describes neural networks as a sequence of computational procedures via a directed graph. In the graph, leaf nodes signify network parameters/input values while the remaining nodes signify various matrix operations upon the input of the same. With CNTK, developers can easily combine and realize model types such as convolutional networks (CNNs), feed-forward DNNs, and recurrent networks (RNNs.)


MXNet is a deep learning framework that offers both flexibility and efficiency. It allows data scientists to mix imperative and symbolic programming to optimize productivity. MXNet has a dynamic dependency scheduler that can parallelize imperative and symbolic operations automatically. In addition to this, MXNet’s graph optimization layer allows for faster symbolic execution.


Pandas is an excellent library for extracting and preparing data for later use in ML libraries such as TensorFlow and Scikit-Learn. It allows for multiple complicated operations over datasets and makes it easy to fetch data from them including Excel, CSV, SQL, JSON files, and more.

Final Word

These are some of the most preferred machine learning frameworks. However, your choice of the ML tool should be informed by your needs since each of them has distinct strengths and weaknesses. Be sure to test out different options before deciding on one.