K-Means vs KNN one of the most frequent interview questions.

KNN represents a supervised classification algorithm that will give new data points accordingly to the k number or the closest data points, while k-means clustering is an unsupervised clustering algorithm that gathers and groups data into k number of clusters. KNN is a parameter that refers to the number of nearest neighbors to include in the majority of the voting process. Let's say k = 5 and the new data point is classified by the majority of votes from its five neighbors and the new point would be classified as red since four out of five neighbors are red.

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Let's look into what companies are looking for from Machine Learning.
Machine Learning Job Requirement -1: Knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.

Machine Learning Job Requirement -2:
Works to design and develop analytical/ data mining/ machine learning models as part of data science solutions.
Gather, evaluate and document business requirements, translate to data science solution definition, and ability to implement the solution on a Big Data platform.
Ability to design and build an end-to-end prototype data science solution to a business problem in any specific sector/ function.
Ability to support and guide end-to-end model lifecycle management Create model documentation as per client/ regulatory standards
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