Whether it is the personal assistants or the intelligent machines working in the manufacturing units, everything is based on the artificial intelligence. In an artificial intelligence process, the machines are automated and have the ability to take decisions on their own. For this reason, training the machines with the available data sets is required and such process is called machine learning. Today, almost every industry is utilizing machine learning as a tool to make the processes more time efficient and cost-effective.
However, it is not easy to make the machines learn by their own. In order to do that, a good knowledge of frameworks and programming is required. For this reason, there are hundreds of machine learning software tools available, but not every one of them is effective. In addition, every application is different and may have different orientations to get results. Thus, it is important to have a clear knowledge of the various software and how they can create new AI systems.
Some of the very common machine learning tools that are used are listed below:
- Google cloud machine learning engine
For those who want to develop machine learning applications by using a larger amount of data and sophisticated algorithms need to work with high-efficiency engines. Google cloud is the hosted machine learning platform that is perfect for such complex models.
- Tensor flow
It is one of the most popular open-source of the ML library which helps in developing applications and platforms. The developer can easily train and create models using the tensor flow’s API. It also boasts some of the best features like libraries, flexible tools, numerical computation, cloud training, and neural network support.
For those who want to use a tool that can have different applications like linear algebra, pattern recognition, statistical data processing, etc. should choose Accord.NET. This tool has various libraries, including audio processing and image processing. It provides around 35 hypothesis tests and 40 statistical distributions to choose from.
- Oryx 2
This ML tool is based on the apache spark and apache Kafka. It is mostly utilized for large scale projects where the machine training has to be performed in real-time. The applications that require regression analysis, packaging, filtering, clustering is developed by using this framework.
- Apache mahout
Apache mahout is an open-source framework that contains a distributed linear algebra and an expressive scale kind DSL. The statisticians, data scientists, and mathematicians can use this tool to promptly implement any algorithm. This tool is used for clustering, classification, recommendation, etc. The libraries that are involved in this field are in vector and matrix forms.
- Apache spark MLib
If the machine learning process requires to access data from varied sources, then this scalable framework is effective. These tools can work in accordance with the Kubernetes, Apache Mesos, cloud or Hadoop. It is very easy to use tools and it has a huge collection of complex and high-quality algorithms like logistic regression, naïve Bayes, k-means, etc.
- Core machine learning tool by Apple
This framework is best for integrating various models of machine learning. This tool is optimized for all types of CPU and GPU and brings out the best performance. The framework is perfect for image analysis in computer vision and natural language processing.
Machine learning is a complex process and in this process, making use of the right framework and library becomes crucial to develop the right application. For this reason, one should know various ML tools and frameworks to become a machine learning and artificial intelligence expert.
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