5 Best open source machine learning Projects built Using python

Machine learning is a mixture of mathematical optimization and statistics, each tutorial disciplines in their very own right. Machine learning is presently driving one thing of a recognition wave. Machine learning is part of computer science, and therefore its practitioners are extremely skilled computer programmers. That being said, let’s highlight 5 Best open source machine learning Projects built Using python .

1 – TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

Google built the underlying TensorFlow software with the C++ programming language. But in developing applications for this AI engine, coders can use either C++ or Python, the most popular language among deep learning researchers. The hope, however, is that outsiders will expand the tool to other languages, including Google Go, Java, and perhaps even Javascript so that coders have more ways of building apps.


2 –   Scikit-learn



3 – Shogun

The Shogun Machine learning toolbox provides a wide range of unified and efficient Machine Learning (ML) methods. shogun seamlessly allows you to easily combine multiple data representations, algorithm classes, and general purpose tools. This enables both rapid prototyping of data pipelines and extensibility in terms of new algorithms.

Shogun also povides wide range of standard and cutting-edge algorithms , core in C++ with unified interfaces to your favourite language , quick prototyping and flexible embedding in workflows . Show combine modern software architecture in C++ with both efficient low-level computing backends and cutting edge algorithm implementations to solve large-scale Machine Learning problems (yet) on single machines.


4 – Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. Caffe encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.

Caffe Speed is perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning. Caffe is the fastest convnet implementation available


5 – Theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.


Theano provides tight integration with NumPy , transparent use of a GPU  efficient symbolic differentiation , speed and stability optimizations , dynamic C code generation , extensive unit-testing and self-verification .



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