Machine learning is changing the way we do things, and it has started becoming mainstream very quickly. While many factors have contributed to this increase in machine learning, one reason is that it is becoming easier for developers to apply it. And, that is through open source frameworks.
If you want to learn about machine learning in a detailed way, there are some resources to guide through it. There are many frameworks available, but there are some which can help a beginner get started with.
TensorFlow was developed by the Google Brain Team to handle perceptual and language understanding tasks. It can also conduct research on machine learning and deep neural networks. TensorFlow has a Python-based interface. It's used in many of Google's products, handling speech recognition, Gmail, photos and search. What's useful about this framework is that it can perform elaborate mathematical computations and see data flow graphs. TensorFlow is flexible, meaning users can write their own libraries on top of it. It's also portable, able to run in the cloud and on mobile computing platforms as well as with CPUs or GPUs.
Amazon Machine Learning:
Amazon Machine Learning (AML) is built for developers, with many tools and wizards to help you create machine learning models without having to learn all the complexities of how machine learning works. With AML, you can generate predictions and use data from Amazon Redshift, the data warehouse Platform as a Service.
Shogun has many state-of-the-art algorithms, making it a handy tool. It is written in C++ and provides data structures for machine learning problems. It can run on Windows, Linux and MacOS. Further, Shogun is helpful because it supports bindings to other machine learning libraries. The list is extensive, but they include: SVMLight, LibSVM, libqp, SLEP, LibLinear, VowpalWabbit and Tapkee.
Accord.NET, a NET machine learning framework, has multiple libraries to handle everything from pattern recognition, image and signal processing to linear algebra, statistical data processing and more. Accord is useful because it has so much to offer, including 40 different statistical distributions, more than 30 hypothesis tests, and more than 38 kernel functions.
Apache Signa, Apache Spark MLlib and Apache Mahout:
Apache Signa, Apache Spark MLlib, and Apache Mahout are three frameworks with a lot to offer. Apache Signa is mostly used in natural language processing and image recognition; it can run over a wide range of hardware. Mahout provides Java libraries and Java collections for various kinds of mathematical operations. Spark MLlib was created with the goal of making machine learning easy. It brings together many learning algorithms and utilities, including classification, clustering, dimensionality reduction and more.