11 Machine Learning Interview Questions

<h1>Machine Learning</h1> <h2>1. What is PCA, why is it helpful, and how does it work?</h2> <p>PCA stands for principal component analysis, and it is a popular dimensionality reduction technique used in machine learning.</p> <p>PCA simplifies the data by reducing the number of features or variables in the dataset while retaining as much information as possible.</p> <p>It&rsquo;s helpful because it (1)&nbsp;<strong>reduces the complexity</strong>&nbsp;of the data, making it easier to visualize and analyze, (2)&nbsp;<strong>eliminates multicollinearity</strong>, which is when two or more features are highly correlated with each other; and can (3)&nbsp;<strong>improve the performance</strong>&nbsp;of ML algorithms by reducing the number of features and eliminating redundant features.</p> <p><a href="https://medium.com/bitgrit-data-science-publication/11-machine-learning-interview-questions-77650cb89918"><strong>Read More</strong></a></p>