Dimensionality Reduction using an Autoencoder in Python

In this 1-hour long project, you will learn how to generate your own high-dimensional dummy dataset. You will then learn how to preprocess it effectively before training a baseline PCA model. You will learn the theory behind the autoencoder, and how to train one in scikit-learn. You will also learn how to extract the encoder portion of it to reduce dimensionality of your input data. In the course of this project, you will also be exposed to some basic clustering strength metrics. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

• Dimensionality Reduction using an Autoencoder in Python
• In this 1-hour long project, you will learn how to generate your own high-dimensional dummy dataset. You will then learn how to preprocess it effectively, before training a baseline PCA model. You will learn the theory behind the autoencoder, and how it is a nuanced, but unsupervised, neural network. You will learn how to train one in scikit-learn. You will also learn how to extract the encoder portion of this trained autoencoder to reduce dimensionality of your input data. In the course of this project, you will also be exposed to some basic clustering strength metrics to evaluate how well your autoencoder works.