Machine learning techniques have grown significantly more powerful in recent years, but excessive complexity in data is still a major problem. There are several reasons for this – distinguishing signal from noise gets harder with more complex data, and the risks of overfitting go up as well. Finally, as cloud-based machine learning becomes more and more popular, reducing complexity in data is crucial in making training more affordable. Cloud-based ML solutions can be very expensive indeed. In this course, Reducing Complexity in Data you will learn how to make the data fed into machine learning models more tractable and more manageable, without resorting to any hacks or shortcuts, and without compromising on quality or correctness. First, you will learn the importance of parsimony in data, 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 and when to resort to feature selection, employing statistically sound techniques to find a subset of the features input based on their information content and link to the output. Finally, you will explore how to use two advanced techniques – clustering, and autoencoding. Both of these are applications of unsupervised learning used to simplify data as a precursor to a supervised learning algorithm. Each of them often relies on a sophisticated implementation such as deep learning using neural networks. When you’re finished with this course, you will have the skills and knowledge of conceptually sound complexity reduction needed to reduce the complexity of data used in supervised machine learning applications.
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
• Understanding the Need for Dimensionality Reduction
  51mins
• Using Statistical Techniques for Feature Selection
  44mins
• Reducing Complexity in Linear Data
  36mins
• Reducing Complexity in Nonlinear Data
  32mins
• Dimensionality Reduction Using Clustering and Autoencoding Techniques
  33mins