Machine Learning has been around for decades, but it has recently received a great deal of attention. Machine learning is the application of computer algorithms that improve on the previously stored and analyzed data and via experience evolve automatically by the application of real-time data obtained from real-time operations. It is now regarded as a distinct segment of artificial intelligence. It is increasingly used for a wide range of tasks, ranging from recognition and voice recognition in speech recognition applications to language understanding in machine translation and application diagnosis.
Machine Learning deals mainly with supervised learning, in which the results of past experience are used to make forward predictions about future data. The most common applications include image processing, speech recognition, natural language processing and speech analysis, e-commerce, customer service, robotics, logistics and manufacturing automation, financial markets etc. The main challenge with this type of artificial intelligence is that it requires the help of humans for the correct formulation of complex algorithms which is only possible if there is a good understanding between the human and machine. Another important aspect is that any specific machine learning algorithm could only be tested if it solves some specific problems.
There are two main approaches to applying machine learning models in a domain: the deep learning approach and thevised learning approach. In the deep learning approach new data is analyzed via supervised training with a predefined set of examples, and the best results are then generated by taking the extracted features of each example and evaluating them. On the other hand in the supervised approach new data is analyzed via unsupervised training with a large number of examples, and the best results are then identified by minimizing the squared value of the errors.
The Machine Learning algorithm is also based on a distributional theory. This basically means that there are multiple classes that could be taken into consideration when computing an output from the previous class. So for example, in the Natural Language Processing (NLP) domain, the main categories are the languages themselves, words used by humans and other things that are part of human communication. Thus in this case you have the supervised inputs such as the actual sentences and the speech content, as well as the required labeled examples or the labeled data that will actually be used in the learning process.
Different Machine Learning methods use various different approaches to solve the Machine Learning algorithm. The two most common methods are supervised learning and deep learning. In the former, the new vocabulary and grammar are learned through a supervised process and on the latter the new vocabulary and grammatical rules are learned through a deep unsupervised process. However, both have their own drawbacks and advantages and are used in different Machine Learning problems. For instance in the natural language processing, the supervised learning uses a database of word containing some common meanings of every word.
For machine learning problems such as speech recognition the approach is generally using the supervised programming language algorithm, which was mentioned above. In this case the output is produced by a greedy algorithm i.e. the function outputs true if and only if the input to the algorithm is acceptable. The greedy high potential problem-solving algorithm is also called the reliance problem. The main advantage with this approach is that the output is always correct provided the human intervention or a simulator is present.
Another Machine Learning method that is used widely in business applications is the pattern recognition. Here the training data consists of naturally occurring patterns in text. The main advantage with this technique is that it can recover many of the most commonly used words that appear in natural languages and also it is capable of extracting highly complex patterns from relatively less analyzed data. On the other hand, Deep Learning method on the other hand is used for image recognition i.e. identifying human faces in pictures. It also has the ability to recognize highly unexpected patterns in images such as those formed by folds in fabric, wrinkles in skin etc.
The last machine learning method we will discuss in this article is the programming language or ML. The main advantage of this machine learning model is that it enables the developers to create programs that are capable of solving business problems. The Machine Learning developer can design a program that can solve a business problem in a generic way. A programmer who has a good background in math, science or programming language can also create such programs. The main limitation with this approach is that programmers have to make the necessary connections between the data obtained through the supervised learning and business problems. Hence, the programmer has to use mathematically correct but general data types in the mathematical programming language.