It is undeniably, machine learning and artificial intelligence have become immensely notorious over the past few years. Also, at the moment, big data is gaining notoriety in the tech industry where machine learning is amazingly powerful for delivering predictions or forecasting recommendations, relied on the huge amount of data.
“Machine Learning is using data to answer questions” — Yufeng Guo
This article deals with the top machine learning algorithms that specify how and where such algorithms can be deployed along with a briefing note on what ML algorithms are and how they work.
Being a subset of Artificial Intelligence, Machine Learning is the technique that trains computers/systems to work independently, without being programmed explicitly. And, during this process of training and learning, various algorithms come into the picture, that helps such systems in order to train themselves in a superior way with time, are referred as Machine Learning Algorithms.
Machine Learning Algorithms chart; Image: Source
Machine learning algorithms work on the concept of three ubiquitous learning models: supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning is deployed in cases where a label data is available for specific datasets and identifies patterns within values labels assigned to data points.
Unsupervised learning is implemented in cases where the difficulty is to determine implicit connections in a given unlabeled dataset. (more want to learn about such learning models, click here)
Reinforcement learning selects an action, relied on each data point and after that learn how good the action was. (Related blog: Fundamentals to Reinforcement Learning- its Characteristics, Elements, and Applications)
In the intense dynamic time, several machine learning algorithms have been developed in order to solve real-world problems; they are extremely automated and self-correcting as embracing the potential of improving over time while exploiting growing amounts of data and demanding minimal human intervention. Let’s learn about some of the fascinating machine learning algorithms;
The decision tree is the decision supporting tool that practices a tree-like graph or model of decisions along with their feasible outcomes, like the chance-event outcome, resource costs and implementation. Over the graphical representation of the decision tree, the internal node highlights a test on the attribute, each individual branch denotes the outcome of the test, and leaf node signifies a specific class label, therefore the decision is made after computing all the attributes. (You probably must curious to learn more about it, read the exclusive blog, Decision Tree in ML).
A Naive Bayes classifier believes that the appearance of a selective feature in a class is irrelevant to the appearance of any other feature. It considers all the properties independent while calculating the probability of a particular outcome, even if each feature are related to each other. Some of the real-world cases of naive Bayes classifiers are to label an email as spam or not, to categorize a new article in technology, politics or sports group, to identify a text stating positive or negative emotions, and in face and voice recognition software.
Under statistics computation, Least Square is the method to perform linear regression. In order to establish the connection between a dependent variable and an independent variable, the ordinary least squares method is like- draw a straight line, later for each data point, calculate the vertical distance amidst the point and the line and summed these up. The fitted line would be the one where the sum of distances is as small as possible. Least squares are referring to the sort of errors metric that are minimized.
It shows the connection amid an independent and a dependent variable and deals with prediction/estimations in continuous values. It describes the impact on the dependent variable while the independent variable gets altered, as a consequence an independent variable is known as the explanatory variable whereas the dependent variable is named as the factor of interest. E.g. it can be used for risk assessment in the insurance domain, to identify the number of applications for multiple ages users. (Related article: How Does Linear And Logistic Regression Work In Machine Learning?)
The Logistic Regression Algorithm work for discrete values, it is well suitable for binary classification where if an event occurs successfully, it is classified as 1, and 0, if not. Therefore, the probability of occurring of a specific event is estimated in the basis of provided predictor variables. E.g. in politics, whether a particular candidate wins or loses the election. If you want to learn more about Logistic regression, read out here.
Top 10 Machine Learning Algorithms
In SVM, a hyperplane (a line that divides the input variable space) is selected to separate appropriately the data points across input variables space by their respective class either 0 or 1. Basically, the SVM algorithm determines the coefficients that yield in the suitable separation of the various classes through the hyperplane, where the distance amid the hyperplane and the closest data points is referred to as the margin.
However, the optimal hyperplane, that can depart the two classes, is the line that holds the largest margin. Only such points are applicable in determining the hyperplane and the construction of the classifier and are termed as the support vectors as they support or define the hyperplane.
Clustering Algorithms refer to the group task of clustering, i.e. grouping an assemblage of objects in such a way that each object is more identical to each other under the same group(cluster) in comparison to those in separate groups. However, each clustering algorithm is different, some of them are Connectivity-based algorithms, dimensionality reduction, neural networks, probabilistic, etc.
Boosting algorithms are used while dealing with massive quantities of data for making a prediction with great accuracy. It is an ensemble learning algorithm that integrates the predictive potential of diversified base estimators in order to enhance robustness, i.e. it blends the various vulnerable and mediocre predictors for developing strong predictor/estimator. These algorithms usually fit well in data science competitions like Kaggle, Hackathons, etc. As treated most preferred ML algorithms, these can be used with Python and R programming for obtaining accurate outcomes.
PCA is a statistical approach that deploys an orthogonal transformation for reforming an array of observations of likely correlated variables into a set of observations of linearly uncorrelated variables, is known as principal components. Its applications include analysing data for smooth learning, and visualization. Since all the components of PCA have a very high variance, it is not an appropriate approach for noisy data.
Deep learning methods are the modern approach to neural networks that uses plentiful computational resources. They are connected with large and complicated neural networks. Also, many methods are involved hugely with comprehensive datasets of the labelled data in terms of images, text, audio and video. Some popular deep learning algorithms are Convolutional Neural Network (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), etc.
From the above discussion, it can be concluded that Machine learning algorithms are programs/ models that learn from data and improve from experience regardless of the intervention of human being. Some popular examples, where ML algorithms are being deployed, are Netflix’s algorithms that recommend movies based on the movies (or movie genre) we have watched in past, or Amazon’s algorithms that suggest an item, based on review or purchase history.
Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal. – Eric Schmidt (Google Chairman)
Not only these examples, but there are also various examples/applications that leverage ML potential at its entirety; some of them are as follows;
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