What is Machine learning?
Machine learning is the advanced scientific study of various algorithms and statistical models that computer programs use in carrying out specific functions independent of explicit instructions by programmers. In machine learning, computer systems access data and use statistical models to infer and make predictions as their resulting output.
The algorithms are the essential part of machine learning and it is the efficiency of these algorithms that determines the effectiveness of the inferences made by these computer systems. The algorithms use sample data, also referred to as training data to learn the patterns, which are later used to predict results for another set of provided data. Machine Learning is often abbreviated as ML, and it is a subset of artificial intelligence.
Artificial Intelligence, AI, is a field of computer science that deals with machines and their ability to mimic cognitive functions similar to natural intelligence by human beings. Artificial intelligence aims at improving the capability of machines to perform various scientific functions, aid in problem-solving and to learn from present and past data. This field of science has grown in the last few decades and computers have reached a point where they can independently understand human speech, make strategic decisions and autonomously control machines in industries or at home.
Machine learning Models
The basic method for machines to learn is to train them. The training of machines entails supplying the machines with ‘training data’ and explicitly teaching them how to identify patterns from the sample data and make predictions from the sample data. Whenever a machine faces a new set of data, it needs to be trained to perform analysis on the new sets of data. In this regard, there are three main classifications of machine learning models. These include supervised learning model, unsupervised learning model and semi-supervised learning models.
Supervised learning model
With this model, the machine is required to apply exactly what has been learned before with the training data and apply the same model on the new set of data. In this model, the machine is required to compare its output and the correct result and subsequently make changes to the model to make it more effective by addressing the errors.
Unsupervised learning model
With this model, the system is not required to provide the right output. Instead, the learning process entails the machine sifting through the data, identifying existing patterns among the unclassified and unlabeled data. The end result is the determination and identification of hidden data structures and relationships among the various sets of data provided.
Semi-supervised learning model
In this model, both the concepts and supervised and unsupervised learning models are encapsulated. During the training of the machines, both labeled and unlabeled data are used. Normally, the model uses more unlabeled data than the labeled data to increase the learning speed.
Data Science Vs Machine learning
Data science is the science of big data and the processes involved in the analysis, preparation and the cleansing of this data. On the other hand, machine learning is the science behind the computing algorithms that are created to extract data, learn from the extracted data, and to make predictions based on the statistical models of the ML algorithms. While these two fields of science are related, they are unique because data science is more focused on the data while the machine learning is more focused on the machine learning processes. The common issue between these two is the data factor.
SAS Machine Learning
SAS is an abbreviation that stands for Statistical Analysis System and is, essentially, a software suite that is built with data in mind. The SAS software is built to deal with advanced forms of data analytics, data management and offering predictive analytics solutions. SAS machine learning is an advanced form of machine learning as it offers the machine learning processes access to various data sources and manages these data sources in ways that can make it possible for machine learning programmers to train their machines from these accessible sources of data.
The SAS machine learning offers various interfaces and solutions that provide various statistical and data modeling features and other powerful tools for viewing and management of the vast accessible data.
How does Machine learning Work?
Machine learning is a complex process that is built to mimic normal human beings learning processes. In this case, the machine system is the human being and the knowledge is the data. Programmers build algorithms to enable the machines to learn at first and the aim of machine learning is to achieve a level of machine understanding whereby the level of human input is zero. In order to learn, the machines are provided with sets of data, known as training data, which they use to learn about patterns and other data processing features. After learning from a given sample, the machine is provided with a similar sample of new data and is expected to perform functions on the new data as it had ‘learned’ from the previous provided sample data. The machine then compares its results with the expected output, identifies the errors and adjusts for these errors in the next phase of data analysis thereby improving its efficiency.
Machine learning is a relatively new field in computer science though its importance is burgeoning by the day. The complexity of the processes involved and the amount of research into the field is a limiting factor for students pursuing knowledge in this area. However, it is advisable to seek computer programming help online to offer guidance on the data science, analytics, and machine learning fields.