It is very important for any data scientist to familiarise themselves with any number of machine learning algorithms. That number is determined by the dozens of routes they want their career to take.
Whether you want to be a ‘jack of all trades, master of one’ or ‘Right tool for the right job’ person, these 10 algorithms will provide any machine learning enthusiast with a steady base from which to kickstart their career.
Here are 10 machine learning algorithms for a career in data science:
This algorithm is modelled by imitating the human brain which interprets the sensory data through a kind of machine perceptions, labelling or clustering raw inputs. The neural networks can be used as a clustering or classification layer on top of the data which is stored and managed.
The K-means clustering is a method which is commonly used to automatically partition a dataset into k groups. The algorithm proceeds by selecting the k initial cluster centres and the iteratively filtering them as each instance are assigned to its closest cluster centre whereas each cluster centre is updated to the mean of its constituent. Finally, the algorithm converges when there is no further change in the assignment of instances to clusters. This method is popular machine learning algorithms, particularly for cluster analysis in data mining.
Linear Regression analysis estimates the coefficients of the linear equation which involves one or more independent variables where the variable which you want to predict is known as the dependent variable and the variable which you are using to predict the other variables is called the independent variable. The simple linear regression is a model which has a single regressor x which has a relationship with a response y that is a straight line.
Hence y=A.x+B; where A is the intercept and B is the slope.
Support Vector Machine is a supervised learning technique which represents the datasets as points. The main goal of SVM is to construct a hyperplane which divides the datasets into different categories and the hyperplane should be at the maximum margin from the various categories. This algorithm helps in removing the over-fitting nature of the samples and provides better accuracy.
This method is basically used for classification of data as well as dimensionality reduction. LDA can easily handle the case where the within-class frequencies are unequal and their performances have been examined on randomly generated test data. This method also helps to better understand the distribution of the feature data.
6. Naive Bayes
One of the basics in terms of machine learning algorithms. This simple classification algorithm is based on the Bayes Theorem. The algorithm aims to calculate the conditional probability of an object with a feature vector which belongs to a particular class. It is called “Naive” because it makes the assumption that the occurrence of a certain feature is independent of the occurrence of other features.
Logistic regression, also known as the logit classifier, is a popular mathematical modelling procedure used in the analysis of data. Regression Analysis is used to conduct when the dependent variable is binary i.e. 0 and 1. In Logistic Regression, the logistic function is used to describe the mathematical form on which the logistic model is based. The reason behind the popularity of the logistic model is that the logistic function estimates that the variable must lie between 0 and 1.
8. Random Forest
Random Forests are basically the combination of tree predictors where each tree depends on the values of a random vector that are sampled independently and with the same distribution for all the trees in the forest. This technique is one of the easiest to use and most flexible of all machine learning algorithms because it can be both used for classification and regression tasks.
Principal Component Analysis forms the basis for multivariate data analysis. This statistical method converts a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. This method is helpful in evaluating the minimum number of factors for the maximum variance in the data.
K-Nearest Neighbours is one of the more essential machine learning algorithms. It is known as the lazy learning, as the function is only approximated locally and all the computations are deferred until classification. The algorithm selects the k-nearest training samples for a test sample and then predicts the test sample with the major class amongst k-nearest training samples.