Dimensionality Reduction

Dimensionality Reduction is a data preprocessing technique used in machine learning, which reduces the number of random variables to consider by obtaining a set of principal variables. Coursera's Dimensionality Reduction catalogue teaches you to handle high-dimensional data, enhance computational efficiency, and prevent overfitting. You'll learn to implement methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Non-negative Matrix Factorization (NMF). You'll also understand how to visualize high-dimensional datasets, improve model performance, and handle issues related to underfitting and overfitting. This knowledge will empower you to tackle complex machine learning problems, data analysis tasks, and make sense of large datasets.