We are living in the most defining period of human history. But what makes it defining is probably not what is happening, but what is coming our way in the coming years.
With the rapid growth of technology, Machine Learning is gaining more importance and has handled an enormous amount of data, hereby making accurate predictions.
There are 3 types of machine learning algorithms, such as –
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Supervised Learning
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Unsupervised Learning
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Reinforcement Learning
Machine Learning algorithms are used for a variety of purposes, such as Image Tagging, Spam filtering, Predictions, Optical Character Recognition and so on.
Decision Trees
Decision Tree, a type of supervised learning algorithm, is a flow-chart esque diagram that shows various outcomes from a series of decisions. It can be used as a decision-making tool either for research analysis, or planning strategy. As a design, it allows you to approach the problems in a more structured and systematic way to arrive at a logical conclusion. It works for both categorical and continuous input/output variables. From a business point of view, the decision tree is the least number of yes/no questions asked, to assess the probability of correct decision making, at most point of time.
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Decision trees are commonly used in the financial world in areas such as spending, approval, and portfolio management. They are also helpful while examining the viability of a new product. The primary advantage of using a decision tree is that it is easy to follow and understand.
Based on the available targets, decision tree can be of two types –
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Categorical Variable Decision Tree
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Continuous Variable Decision Tree
Linear Regression
Linear Regression is a statistical procedure for predicting the value of a dependent variable from an independent variable, where the relationship between the variables can be explained using a linear model.
The simplest form of equation with a dependent and independent variable can be written as y=c+b*x, where,
y=estimated dependent score, c=constant, b=regression coefficients, x=independent variable
Below is the list of different linear regression analysis available –
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Simple Linear Regression
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Multiple Linear Regression
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Logistic Regression
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Ordinal Regression
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Multinomial Regression
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Discriminate analysis
Naïve Bayes
The Naïve Bayes model works on the concept that every feature is independent of another feature. Even if a relation exists, it considers each of them individually while calculating the probability of an outcome. The technique is based on Baye’s Theorem.
The Naïve Bayes model is easy to build, and especially useful for a very large set of data. Apart from simplicity, Naïve Bayes is known to outperform highly sophisticated classification methods.
Logistic Regression
Logistic regression is a classification algorithm which is a powerful statistical way of modeling a binomial outcome. It determines the relationship between the categorical dependent and one or more independent variables, by estimating probabilities using a logistic function. In a way, it predicts the probability of event occurrences by fitting data to a logit function.
Regressions are used in real world applications such as –
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Predicting the revenues of certain product
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Credit Scoring
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Adding Interaction Term
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Using a nonlinear model
Ensemble Methods
Ensemble Methods are learning methods that create multiple models and combine them to produce improved results. This method normally produces more accurate solutions compared to the single model.
The actual ensemble method is the Bayesian, whereas the recent algorithms include error correcting output, coding, bagging, and boosting.
Ordinary Least Squares Regression
Ordinary Least Square (OLS) regression, a statistical analysis method, is used to estimate the relationships between one and more independent variables, and a dependent variable. This is done by minimizing the sum of the squares of the result obtained in the difference between the observed, and the predicted values of the dependent variable configured as a straight line.
OLS regression is a common technique used to design a single response variable that has been recorded at least on an interval scale. This technique may be applied to single or multiple explanatory variables, and categorical variables that are approximately coded.
Support Vector Machines
A Support Vector Machine (SVM) is a selective classifier which is formally defined by a separating hyperplane. SVM is a universal constructive learning technique, and is from the field of machine learning which is applicable to both classification and regression. SVM delivers state of the art performance in a real-time application such as text categorization, hand-written character recognition, image classification, bio sequence analysis, and so on. These are now established as one of the standard tools for machine learning and data mining.
Clustering Algorithms
Clustering is termed as one of the most important learning techniques which deals with finding a structure among the collection of unrelated data. In other words, it is the process of organizing objects into groups that are similar in some way. Therefore, a cluster is a collection of objects that are similar between them, and dissimilar to the objects belonging to other clusters.
Principle Component Analysis
Principle Component analysis (PCA) is a technique that helps in machine learning, signal processing, and image compression. It is used as a modification to convert a set of correlated variables into a set of uncorrelated variables called principal components.
The main idea of PCA is to reduce the dimension of a set of data consisting of many correlated variables, either heavily or lightly, while retaining the variations present in the dataset, to the maximum extent. In a way, it is used to reduce dimensions of data without much loss of information.
Singular Value Decomposition
Singular Value Decomposition (SVD) is a factorization of the real or complex matrix. SVD gives a convenient way for breaking the matrix into simpler and meaningful pieces.
Independent Component Analysis
Independent Component Analysis (ICA) is a standard data analysis technique applied to an array of problems in machine learning and signal processing.
In other words, it is a statistical technique used for revealing the hidden factors that determine the set of random variables, measurements, or signals. In this model, data variables are assumed to be a linear mixture of some unknown variables and mixing system.
KNN (K-Nearest Neighbor) Algorithm
K Nearest Neighbor (KNN) algorithm is very simple to understand, but works incredibly well in practice. This algorithm can be used for both classification regression problems. However, it is more often used to solve the classification problems.
K Algorithm
To learn about K algorithm, visit the below link:
https://sites.google.com/site/dataclusteringalgorithms/k-means-clustering-algorithm
The technology industry is developing rapidly each day. If you are keen to master machine learning algorithms, start at once. Take up problems, develop a physical understanding, apply these codes and see the fun. We are sure you will find it all interesting, and your career will boost as a result.
We hope this article helped you understand and get acquainted with the most important algorithms for machine learning engineers. Do you find it useful? What aspects of algorithm confuse you the most? Feel free to post your thoughts in the comments section below.
