With the advancements in the field of artificial intelligence and machine learning, it has become essential to understand the fundamentals behind such technologies. This article on Principal Component Analysis will help you understand the concepts behind dimensionality reduction and how it can be used to deal with high dimensional data.
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Need for Principle Component Analysis (PCA)
In general, machine learning works wonders when the dataset provided for training the machine is large and concise. Usually, having a good amount of data lets us build a better predictive model since we have more data to train the machine with. However, using a large data set has its own pitfalls. The biggest pitfall is the curse of dimensionality.