The term machine learning came into existence in the year 1959, by Arthur Samuel. The term has originated from the immense research based on robotics and cybernetics. However today, the term mainly revolves around the concepts of mathematical algorithms. This is a technology that has been specifically designed for performing certain types of tasks, which include:
- Storage of sensory information
- Data compression
- Decision making and control
- Detection of the repetitive pattern of data and its classification
- Regression of analysis of noisy patterns
- Finding new patterns
The term machine learning, thus, is not just one technology or technique but includes the utilization of multiple machine learning technologies for the creation of systems, which learn from the data. It not only includes learning new data but also facilitates making predictions and taking actions, given a new situation. Machine learning is related deeply with subjects like modern mathematics, statistics, set theory, probability, calculus, and dynamic systems, to name a few.
Machine learning algorithms: The algorithms cannot be preprogrammed because the context of the application may vary. A broad range of algorithms is used instead, for a particular condition and the various parameters are learned to fit properly in a particular application’s data. The data that is programmed is referred to as the ‘training data’. The algorithms are used in various fields of email filtering, computer vision, finding of network intruders, etc.
Machine learning tasks: The tasks of machine learning are broadly divided into supervised learning, wherein the algorithm is given a set of both input and output data and builds a mathematical model thereafter. The regression algorithm and the classical algorithm are two classic examples of the supervised learning. The semi-supervised method includes building a mathematical model from the incomplete information set. In the case of unsupervised learning, the model is based only on inputs, and no outputs are given. This method is mainly used for finding out the clusters of data.
Techniques in machine learning:
- Regression: The method enables making predictions in terms of values. It can enable making predictions about spotting valuable customers, to predict the correct pricing of an object to be sold in order to make a maximum profit or even predict house pricing.
- Classification: This technique falls under the supervised method. By making use of this method, data is classified into various datasets.
- Clustering: This is a method of unsupervised classification, wherein the data is simply segregated into clusters. Within a data, there can be several clusters, depending upon the need for classification.
- Anomaly detection: This is the exact opposite method of classifying and clustering. Here, the main focus relies upon finding out the outliers.
Open source machine learning technologies: Recently, there has been a rapid increase in growing interest in artificial intelligence and machine learning, and the wonders they can do. Many professionals are trying to combine their expertise on the various available technologies and language in order to make new ones. Given below are some of the best open source technologies that can be used for taking machine learning to an entirely new level.
- Scikit-learn: The technology has been released in 2007 and has been written in Python. It gives various models for machine learning which make use of regression, classification, clustering, which have been discussed earlier. The technology focuses primarily on data analysis and data mining.
- Keras: Keras was released in 2015 with a view to simplifying the process of creating deep learning models. The technology has been written in Python and enables deployment over other technologies like Theano, TensorFlow and Microsoft Cognitive Toolkit. The specialty of the technology lies in it being user-friendly, ease of extensibility and its modularity. It functions well both on Central Processed Units (CPUs) and on Graphic Processed Units (GPUs).
- Microsoft Cognitive Toolkit: It was released in the year 2016 and was previously called the CNTK. The advantage of this technology is the capability of handling data from C++, Python or BrainScript. In addition, it also provides ease of integration, provides efficient resource use and good interpretation.
- Theano: This technology was one of the oldest developed technologies which set up a standard in terms of developing deep learning. It enables to style different models of machine learning with ease. It helps in making the processes like optimization, defining, evaluating some mathematical expressions simplified. In addition, it also enables extensive code-testing abilities, thus making it such a powerful tool.
Before starting any machine learning application, you must be aware of all the machine learning technologies available to choose the best suitable one for you.