Machine learning tools

What are machine learning tools?

Machine learning is an astonishing technology, if you use it in a correct way. How fascinating it would be to build a machine that behaves like a human being to a great extent. Mastering machine learning tools will let you play with the data, train your models, discover new methods, and create your own algorithms.

Machine learning comes with an extensive collection of ML tools, platforms, and software. Moreover, ML technology is evolving continuously. Out of a pile of machine learning tools, you need to choose any of them to gain expertise. 

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Which machine learning tool is the best?

1. Knime     

Knime is again an open-source machine learning tool that is based on GUI. The best thing about Knime is, it doesn’t require any knowledge of programming. One can still avail of the facilities provided by Knime. It is generally used for data relevant purposes. For example, data manipulation, data mining, etc.

Moreover, it processes data by creating different various workflows and then execute them. It comes with repositories that are full of different nodes. These nodes are then brought into the Knime portal. And finally, a workflow of nodes is created and executed.

2. Accord.net

Accord.net is a computational machine learning framework. It comes with an image as well as audio packages. Such packages assist in training the models and in creating interactive applications. For example, audition, computer vision, etc.     

As .net is present in the name of the tool, the base library of this framework is C# language. Accord libraries are very much useful in testing as well as manipulating audio files.

3. Weka

Weka is an open-source software. One can access it through a graphical user interface. The software is very user-friendly. The application of this machine learning tool is in research and teaching. Along with this, Weka lets you access other machine learning tools as well. For example, R, Scikit-learn, etc.  

4. RapidMiner        

RapidMiner is a piece of good news for the non-programmers. It is a data science platform and has a very amazing interface. RapidMiner is platform-independent as it works on cross-platform operating systems.

With the help of this tool, one can use their own data as well as test their own models. Its interface is very user-friendly. You only drag and drop. This is the major reason why it is beneficial for non-programmers as well. 

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5. Google Cloud AutoML

The objective of Google cloud AutoML is to make artificial intelligence accessible to everyone. What Google Cloud AutoML does is, it provides the models which are pre-trained to the users in order to create various services. For example, text recognition, speech recognition, etc. 

Google Cloud AutoML became very much popular among companies. As the companies want to apply artificial intelligence in every sector of the industry but they have been facing difficulties in doing so because there is a lack of skilled AI persons in the market. 

6. Jupyter Notebook     

Jupyter notebook is one of the most widely used machine learning tools among all. It is a very fast processing as well as an efficient platform. Moreover, it supports three languages viz. Julia, R, Python. 

Thus the name of Jupyter is formed by the combination of these three programming languages. Jupyter Notebook allows the user to store and share the live code in the form of notebooks. One can also access it through a GUI. For example, winpython navigator, anaconda navigator, etc. 

7. Apache Mahout     

Mahout is launched by Apache which is an open-source platform based on Hadoop. It is generally used for machine learning and data mining. Techniques such as regression, classification, and clustering became possible with Mahout. Along with this, it also makes use of math-based functions such as vectors, etc. 

8. Azure Machine Learning studio

Azure machine learning studio is launched by Microsoft. Just like, Google’s Cloud AutoML, this is Microsoft’s product which provides machine learning services to the users. Azure machine learning studio is a very easy way to form connections of modules and datasets. 

Along with this, Azure also aims to provide AI facilities to the user. Just like TensorFlow, it also works on CPU and GPU. 

9. MLLIB

Like Mahout, MLLIB is also a product of Apache Spark. It is used for regression, feature extraction, classification, filtering, etc. It also often called Spark MLLIB. MLLIB comes with very good speed as well as efficiency.     

10. Orange3

Orange3 is a data mining software which is the latest version of the Orange software. Orange3 assists in preprocessing, data visualization, and other data-related stuff. One can access Orange3 through the Anaconda Navigator. It is really very helpful in Python programming. Along with this, it can also be a great user interface. 

11. IBM Watson     

IBM Watson is a web interface that is given by IBM for using Watson. Watson is a human interaction Q and A system which is based on Natural Language processing. Watson is applied in various fields such as automated learning, information extraction, etc. 

IBM Watson is generally used for research and testing purposes. Its objective is to offer a human-like experience to the users. 

12. Pylearn2

Pylearn2 is a machine learning library that is built on top of Theano. Therefore, there are many functions that are similar between them. Along with this, it can perform math calculations. Pylearn2 is also capable of running on the CPU and GPU as well. Before getting to Pylearn2, you must be familiar with Theano.

13. SageMaker

Amazon SageMaker is a cloud machine-learning platform that enables developers to create, train, and deploy machine-learning models in the cloud. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices.

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Where are machine learning tools used today?

Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc.

The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data. Machine learning applications provide results on the basis of past experience.