Key Takeaways -   To understand data science, one needs a lot of technical expertise along with business understanding. Generative AI, MLOps, and clou ...

In the previous three articles, I explained the mechanism of PCA from scratch. Because you have the experience of manual calculations with NumPy, you understand what the library is doing behind the ...

The data used in this article is cited directly from the data provided in the textbook. For data with a small number of entries, we register the data in the code, and for data with a large number of ...

Firm principals must adapt to a changing business and management environment. Here’s how to become a better architecture firm leader. Everyone knows what a Principal is: a strong performer in a firm ...

PCA is an important tool for dimensionality reduction in data science and to compute grasp poses for robotic manipulation from point cloud data. PCA can also directly used within a larger machine ...

Dimensionality reduction simplifies high-dimensional data into a small number of representative patterns. One dimensionality reduction method, principal component analysis (PCA), often selects ...

Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction in data analysis and machine learning. It aims to transform high-dimensional data into a ...

Abstract: Stata and python were used to analyze and clean the data of TCM diagnosis thyroid medical records. Principal component analysis and factor analysis were used to analyze and clean the ...

A system that is capable of automatically irrigating the agricultural field by sensing the parameters of soil in real-time and predicting crop based on those parameters using machine learning. The ...