Do I need to learn every Machine Learning algorithm out there to become a Machine Learning Engineer?

Question

Could someone tell me whether I should learn every Machine Learning algorithm to become a Machine Learning Engineer?

Answer 1

Though it would be beneficial if you had brief knowledge of all the Machine Learning algorithms, it is not necessary for you to learn them all in-depth. However, some of the algorithms that you need to have a clear understanding of in order to become a Machine Learning Engineer are mentioned below:

• SVM: SVM stands for Support Vector Machine. It is a supervised Machine Learning algorithm that allows you to overcome regression and classification challenges. Using this method, professionals can plot data in an n-dimensional space wherein ‘n’ represents the number of features and all the values plotted in specific coordinates. Further, you can differentiate the two classes using the classification technique.

• Bayesian Networks: Bayesian Networks are models that allow you to create models using data. These models can be useful to perform a variety of processes, such as anomaly detection, prediction, automated insights, diagnostics, time series prediction, reasoning, and making business decisions, etc. The four major disciplines, which are diagnostic analytics, descriptive analytics, prescriptive analytics, and predictive analytics.

• Random Forests: It is an easy-to-learn Supervised Machine Learning algorithm. It is highly popular in numerous industries due to its diversity and simplicity. It helps in developing several decision trees and combines them to get accurate predictions.

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