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Presently, nearly all manual tasks are being automated. Machine learning algorithms are changing the definition of manual. It is very evident that machine learning is one of the hottest trends in the tech industry and is incredibly powerful to make predictions, and calculated suggestions based on large amounts of data. Machine learning engineers should be thorough with the routine algorithms to understand ML operations and execute advanced techniques.
Here are the top 10 machine learning algorithms every engineer should know.
• Linear Regression: In this process, a relationship is established between independent and dependent variables by fitting them into a line. It demonstrates the impact on the dependent variable when the independent variable has changed in any way. An example of a linear regression algorithm is its usage for risk assessment in the insurance domain.
• Support Vector Machine Algorithm: It is used for classification or regression problems. The data is divided into different classes by finding a particular line that segregates the data set into multiple classes. The support vector algorithm tries to find the hyperplane that maximizes the distance between these classes so that the classification of data is more accurate.
• Naive Bayes Algorithm: This ML algorithm is based on the Bayes Theorem of Probability, and on applying it yields strong independent assumptions between the features. This model is easy to build and is useful for large datasets. It is simple to use and is known to outperform even highly sophisticated classification methods.
• K Means: It is an unsupervised learning algorithm that solves clustering problems. Data sets are classified into several clusters in such a way that all the data points within a cluster are homogenous and heterogenous than other clusters. This algorithm uses a K number of clusters to operate on a given data set.
• Decision Tree: A decision tree is one of the most popular algorithms used today. It is a supervised learning algorithm that is used for classifying problems. It works well for classifying both categorical and continuous dependent variables.
• Apriori Algorithm: This machine learning algorithm generates association rules using the IF_THEN format. With the help of these association rules, the algorithm determines how strongly or weakly two objects are connected. It is an iterative process for finding the frequent datasets from the large datasets.
• Logistic Regression: Logistic regression is used to discrete values from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. Including interaction terms, eliminating unnecessary features, and regularizing techniques could help improve the performance of the logistic regression algorithm.
• Random Forest Algorithm: A collection of decision tree algorithms is called random forest. Each tree is classified individually to identify a new object based on its attributes. Each forest chooses the classification having the most votes.
• Dimensionality Reduction Algorithms: Vast amounts of data are being stored and analyzed by companies, government agencies, and research organizations. Dimensionality reduction algorithms like decision trees, factor analysis, and random forest can help find relevant details efficiently.
• Gradient Boosting and AdaBoosting Algorithm: These are boosting algorithms that are used when massive loads of data have to be handled to make high accuracy predictions. It combines multiple weak and average predictors to build a strong data predictor.
