Artificial intelligence (AI) is one of the trending topics in today’s technological world. Science has seen a tremendous advancement in this field. Machine learning is a small, but integral part of AI. In this article, you will get a basic understanding of machine learning. You shall see the various types of machine learning and programming languages.
The main objective of machine learning is to understand the data structure, and fit it into a machine so their utility is increased. Although associated with the field of computer science, machine learning is distinct from traditional approaches to computing. With the inputs as commands, computers provide outputs values that lie within a specified range. Thus, machine learning helps automate the decision-making processes by building data models.
Though great advancements have been made, the field of machine learning is a continuously developing one. Now we will see the common methods of machine learning i.e., supervised learning and unsupervised learning. Later, we will consider the prominent algorithmic approaches such as decision tree learning and deep learning. Finally, we will also see the various programming languages used and their attributes.
In supervised learning technique, we provide the computers with example inputs labeled with the desired outputs. This helps the system develop a model by learning from the given data. The labeled output enables the machine to find errors and rectify the model accordingly. In this way, patterns are made so that computer can predict output values when unlabeled data is given.
For example, a machine may be fed data with car images labeled as four wheelers, and images of bikes labeled as two-wheelers. By adequate training, the machine will be able to identify unlabeled car images as four wheelers and images of unlabeled bikes as two-wheelers.
Supervised learning is commonly used to predict future events with the help of historical data. It can train the system to detect spam emails by sharing samples of the same.
This is a more complex method as compared to supervised learning. The machine is fed with unlabeled data and left to find correlations on its own. The quantity of unlabeled data is very high as compared to labeled data. So, unsupervised learning methods are very resourceful and application-oriented too.
All data cannot be labeled. Unsupervised learning directs the machine to find patterns that lie hidden in the given data inputs. This enables the learning algorithms to detect patterns, which are sometimes invisible to humans. Classification of raw data is one of the main applications of unsupervised learning.
Unsupervised learning is particularly used in the field of customer transactions. Large data sets of customers and their transactions may seem gibberish to human eyes. But with the help of unsupervised learning, you can find patterns in it. The learning algorithm may determine the rising popularity of a particular type of soap among men of a certain age range. This can help the company to improve their products accordingly, and provide necessary advertisements. Human resources can be better employed too, working seamlessly towards the cause.
Without receiving any labeled data, unsupervised learning algorithms can classify complex data in meaningful ways. These methods find great applications in fraudulent credit card transactions by detecting anomalies in the purchase. Recommendation systems make use of this type of machine learning as a vital aid in meeting their objectives.
Machine learning derives most of its approaches from statistical techniques. So, if you have some basic knowledge in statistics, it can help in learning machine language approaches better.
Correlation and regression are the most widely used statistical techniques. These help in determining the relationship between quantitative variables. When two variables with unknown dependencies are given, correlation can be used to measure the association between them. Regression techniques are used when a dependent and an independent variable are present. Future values can be predicted with good accuracy, using these statistical methods.
Newer approaches to machine learning are being continuously developed. We will discuss two of the major ones used in machine learning.
Decision Tree Learning
Decision trees are used to assist the decision-making process by the visual representation of various options. In the context of data mining and machine learning, decision tree models help machines provide required outputs by allocating predictive power to the learning algorithm.
In the decision tree, characteristics of data are represented by branches and the possible inferences by leaves. The input data is divided into various subsets based on data attributes. These subsets are tested recursively until a matching target is reached.
The decision tree can be used to make a simple yes or no decision. Alternatively, it can be used to choose among a wide array of decisions. By proper implementation, decision tree approach can prove itself tremendously helpful in the domain of machine learning.
Deep learning approach derives its inspiration from the biological neural networks. It consists of multiple artificial neural network layers comprised of GPUs and hardware. It tries to imitate the human brain. Deep learning uses multilayer processing units to extract information from data, and then processes it. Algorithms can be both supervised and unsupervised. The former can help in classifying the data, while the latter assists in recognizing patterns.
Deep learning approach is capable of processing large amounts of data as compared to other algorithms. This makes it a preferable approach in the field of Artificial Intelligence. Machines employing deep learning techniques have even beaten humans at certain cognitive tasks.
Speech recognition machines, computer vision, etc., are some areas that have vastly benefitted from deep learning. Apple’s Siri and IBM Watson are the reputed systems that employ deep learning techniques.
There are many programming languages today. However, in the field of machine learning, Python is the most desirable language. Other prominent ones are Java, R Programming, and C++. Most of the machine learning jobs require a basic understanding of Python.
Python has a wide array of deep learning frameworks available. This may be the reason for its wide popularity. It can be utilized as a scripting language, and the readable syntax makes it powerful. These properties enable Python to directly work with data, or even preprocess data. Several existing Python packages have updated their library with machine learning functions.
R Programming is the most preferred language as far as data analytics is concerned. It is included in most of the Analytics courses. There are many job opportunities in the field of Google Analytics. When you want to track the usage of a website, Google analytics is used with the help of R Programming. Statistical computing and predictive models make R programming very useful in machine learning.
Engineers and embedded software developers have a good command over C++ language. This makes it a good choice for its usage in the field of robot applications, games, etc. Many machine learning libraries can be integrated into this.
In this article, we have seen the various approaches to machine learning. We have learned about supervised and unsupervised learning. We also have seen the various programming languages associated with this field.
We hope this article has given you a basic understanding of machine learning. If you are interested in this field and have knowledge of programming languages, you have a lot of options. Join freelancer.com and check out the various opportunities for you.