Machine learning is a branch of artificial intelligence, which deals with the acquisition and processing of the information. As a rule, machine learning algorithms are used to classify, categorize, group, rank, and discriminate the data. Generally, the machine learning algorithms collect data, filter out irrelevant information, and then classify or group it according to a pre-defined set of rules.
This kind of algorithms can also be used for diverse purposes and can have general goals in mind. These objectives include the classification of data, the identification of business problems, the categorization of information, the segmentation of users, and the differentiation of users into different groups.
As the tasks and the nature of machine learning solutions change, so do the methods and approach also get changes.
What are the types of Machine Learning Techniques
There are various types of machine learning techniques used to acquire, store, and process data.
- Statistical methods are the oldest and most commonly used machine learning techniques. They are used in many aspects of science. They may be applied in the context of decision making, prediction, pattern recognition, computer-aided design, and computational linguistics. In the field of business, they are used to dealing with customer behavior, communication, employee behavior, sales management, and the detection of fraud.
- Learning algorithms are another type of machine learning solution, which operates by learning and adapting itself to new situations. Learning algorithms are commonly used in solving complex and/or high-dimensional problems.
There are also two types of Learning algorithms
- Supervised learning algorithms are used for classification, data mining, classification by using clustering techniques, clustering by use of principal component analysis, and clustering by use of K-means clustering.
- Unsupervised learning algorithms are used for tasks that are not classified as a part of supervised learning tasks. Examples of such tasks are image recognition, sentiment analysis, machine translation, speech recognition, and machine translation.
So, how to utilize big data as a means of solving the problems is faced by many entrepreneurs? Big data can also be defined as data that is generated by a lot of sources that either come from one source or from many sources.
Big data can be obtained through the use of the internet or through some other third party sources. It may also be acquired through various sources like social media sites, blogs, SMS or other sources.
In order to build a model using the information obtained from the big data, machine learning methods are applied. This technology allows us to access huge amounts of data as if it were one single source, and the machine learning algorithm is applied to extract relevant information from the large database of the big data and put it into a usable form.
Among the basic machine learning techniques, classification is one of the most important as it enables you to categorize the data in a more concise and accurate manner. In classification, a label, such as “person”, “group”, “city”, “product”, “service”, etc., is provided, and then the data is classified based on the type of the labels.
Classification by use of the machine learning algorithms may involve classification by use of one or more of the predefined classes, classifying data according to a set of rules, labeling data by location or time, and classification by use of some kind of relationship or the other. Examples of such relationships are demographics or association.
Such kind of classification may also involve classification by use of multiple types of criteria, where multiple criteria are interrelated and are likely to produce the same results. In this case, the machine learning algorithm is able to make use of multiple criteria in order to classify data. It is also important to note that classification can also be classified by the use of several variables, where multiple variables are used in combination to classify the data.