Dimensional Reduction and Principal Component Analysis -- II


In the previous post, we saw why we should be interested in Principal Component Analysis. In this post, we will do some deep dive and get to know how this is implemented. Now that you have some idea about how to change higher dimensions to lower dimensions, we will go through the below description which is shown in a jupyter notebook. I have downloaded the data of three companies that are in the Indian stock market from Quandl. We will try to understand the Indian ecosystem using this.

Principal Component Analysis


In this post, we will learn about Principal Component Analysis (PCA) -- a popular dimensionality reduction technique in Machine Learning. Our goal is to form an intuitive understanding of PCA without going into all the mathematical details.

'What Are The Limits of AI?' Lewis Liu, CEO, Eigen Technologies Artificial Lawyer


Readers will probably have heard of Eigen and the work it is doing in the legal AI doc review space, but perhaps have not had a chance to hear its inspiring founder speak. Here is a short video presentation (13.27 mins) recorded at a recent Codex.com'So

Understanding Principal Component Analysis – Hacker Noon


The purpose of this post is to give the reader detailed understanding of Principal Component Analysis with the necessary mathematical proofs. We plot the data and find various patterns in it or use it to train some machine learning models. One way to think about dimensions is that suppose you have an data point x, if we consider this data point as a physical object then dimensions are merely a basis of view, like where is the data located when it is observed from horizontal axis or vertical axis. As the dimensions of data increases, the difficulty to visualize it and perform computations on it also increases. Variance: It is a measure of the variability or it simply measures how spread the data set is. Mathematically, it is the average squared deviation from the mean score.