Top Machine Learning Algorithms || UNIV_TECHS

ML algorithms study is regarded to be the ‘Sexiest job of the 21st century’ as shown in the Harvard Business Review article. For beginners who are eager to study Machine learning basics, here is a great quick guide to the top 10 Machine Learning Algorithms used by ML programmers that you must know.

Machine Learning algorithms do not require human intervention, they are able to study data and advance from experience. Learning data entails studying the function that plots input and output and studying the unseen structure from unlabeled data. Ensure you choose the right machine learning task that is appropriate to your problem. Try different Algorithms for every problem to evaluate their performance and then choose the best.

Types of Machine Learning Algorithms

A basic understanding of varying types of ML Algorithms will enable you to understand better on how algorithms work.

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The Top Machine Learning Algorithms

 1. Linear Regression

Linear regression is the easiest of all algorithms to understand. It shows the relationship of variable(x) in relation to variable(y). It illustrates the effects of a dependable variable where change occurs in the independent variable. Its commonly used for presentations by insurance firms, forecasting sales, and analyzing risk assessment in health.

2. Logistic regression

This technique is effective for the binary classification. In logistic regression, the output is a probability that ranges from 0 to 1, unlike linear regression where an output is produced directly. logistic regression can be used in events that require more logic for a prediction like when forecasting weather conditions, during voting to know whether people will vote for a certain candidate or not. The objective of logistic regression is to reduce the error between the forecast outcome and the real outcome by using the training statistics to know values of coefficients b0 and b1. Maximum Likelihood Estimation technique is used to estimate the coefficients.

3. Classification and Regression Trees

The decision tree includes the non-terminal nodes represented by the root node and internal node and terminal nodes. Terminal nodes are represented by leaf nodes.

A single input variable (x) presents a certain non-terminal node and a split point on that particular variable. The leaf node shows output variable (y) which makes a prediction. When using the tree model to make predictions, you walk through the splits of the tree up to the leaf node and output the value shown at the leaf node.

Unsupervised learning algorithms

1. Apriori

Market basket analysis widely uses Apriori algorithms to look for combinations of things that regularly co-appear in databases. The relationship rules are made after the threshold for support is crossed. The principal behind Apriori is that if a set of items is common, then all subsets related must be common.

2. K-means

K-means is a Machine Learning Algorithm used to collect related data into clusters. It works by classifying unstructured data into different ‘K’ groups ‘Each dataset comprises a collection of structures and the algorithm orders the unstructured data and classifies them in relation to specific features.

3. Principal Component Analysis

Principal Component Analysis (PCA) is used for speeding exploration of data and making correlations by minimizing the number of variables. Maximum variance in the data is taken into a different coordinate structure with axes named ‘principal components’. It categorizes patterns in items and targets to make correlations of variables in the items data. Whatever relationships the PCA finds is shown on a similar (but smaller) dimensional structures. The algorithm is used in applications such as, stock market forecasts, gene expression study and in pattern grouping tasks that overlook class labels.