Dimensionality Reduction is a powerful and versatile machine learning technique that can be used to improve the performance of virtually every ML model. Using dimensionality reduction, you can significantly speed up model training and validation, saving both time and money, as well as greatly reduce the risk of overfitting. In this course, Reducing Dimensions in Data with scikit-learn, you will gain the ability to design and implement an exhaustive array of feature selection and dimensionality reduction techniques in scikit-learn. First, you will learn the importance of dimensionality reduction, and understand the pitfalls of working with data of excessively high-dimensionality, often referred to as the curse of dimensionality. Next, you will discover how to implement feature selection techniques to decide which subset of the existing features we might choose to use, while losing as little information from the original, full dataset as possible. You will then learn important techniques for reducing dimensionality in linear data. Such techniques, notably Principal Components Analysis and Linear Discriminant Analysis, seek to re-orient the original data using new, optimized axes. The choice of these axes is driven by numeric procedures such as Eigenvalue and Singular Value Decomposition. You will then move to dealing with manifold data, which is non-linear and often takes the form of swiss rolls and S-curves. Such data presents an illusion of complexity, but is actually easily simplified by unrolling the manifold. Finally, you will explore how to implement a wide variety of manifold learning techniques including multi-dimensional scaling (MDS), isomap, and t-distributed Stochastic Neighbor Embedding (t-SNE). You will round out the course by comparing the results of these manifold unrolling techniques with different datasets, including images of faces and handwritten data. When you’re finished with this course, you will have the skills and knowledge of Dimensionality Reduction needed to design and implement ways to mitigate the curse of dimensionality in scikit-learn.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  2mins
• Getting Started with Feature Selection in scikit-learn
  66mins
• Dimensionality Reduction in Linear Data
  42mins
• Dimensionality Reduction in Non-linear Data
  38mins