7 Machine Learning Algorithms Every Engineer Should KnowMachine Learning, the branch of Artificial Intelligence is based on the idea that machines should be able to learn and adapt through experience. It is increasingly gaining popularity over the last couple of years.

Machine learning is one approach to achieve Artificial Intelligence by using algorithms. It is predicted that Machine Learning Algorithms may replace a wealth of jobs in the coming years. So here are 7 Machine Learning Algorithms that every engineer should know:

1. Logistic Regression

Logistic Regression is a powerful statistical way of estimating discrete values (usually binary values) from a set of independent variables. The algorithm helps in the prediction of an event by fitting data to a logistic function. The Logistic Regression models can be improvised by regularizing techniques using a non-linear model and by adding interaction terms. Regressions can be used in real life applications like credit scoring, calculating the success rate of marketing campaigns, predicting the traffic in a certain area etc.

2. Decision Trees

Decision Trees is a supervised learning algorithm that is used to classify problems. The classification is done based on the most significant attributes of variables to make many distinct groups. It uses a tree like model where each node of the tree can help one interpret the consequence of selecting that node or option. The algorithm helps in approaching a problem in a structured and systematic way to come to a logical conclusion.

3. Naïve Bayes Classifier Algorithm

The Naïve Bayes Classifier Theorem works on the popular Bayes Theorem of Probability. The classifier works on assumption that every feature is independent of another feature. Even if the features are related to each other the classifier would consider all of these properties independently when calculating the probability of a particular outcome. It uses the massive data sets efficiently. This algorithm is used to build machine learning models for Disease Prediction and Document Classification. Naive Bayes is used for face recognition software, to mark an email as spam or not and in many more real world problems.

4. K-Means Clustering Algorithm

K-means clustering algorithm is a popularly used unsupervised machine learning algorithm for cluster analysis. Data sets are classified in a particular number of clusters in such a way that all the data points within a cluster are homogeneous and heterogeneous from the data in other clusters. K-Means is a non-deterministic and iterative method. This algorithm helps in reducing the computational time as Search Engines like Yahoo, Google use this Clustering Algorithm to cluster pages by similarity.

5. Support Vector Algorithm

Support Vector Algorithm is a supervised machine learning algorithm where raw data is plotted in the n-dimensional plane. The classification of data is made easy by tying the value of each feature to a particular coordinate. Lines called as classifiers split the data and plot them on a graph. Some of the biggest problems that have been solved using SVMs in terms of scale are image-based gender detection, display advertising, stock market forecasting etc.  The rich features of SVM are that it does not make any strong assumptions on data and offers best classification performance.


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