Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM.
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Dimensionality reduction techniques are based on unsupervised machine learning algorithms and their application offers several advantages. In this course you will learn how to apply dimensionality reduction techniques to exploit these advantages, using interesting datasets like the MNIST database of handwritten digits, the fashion version of MNIST released by Zalando, and a credit card fraud detection dataset. Firstly, you will have a look at t-SNE, an algorithm that performs non-linear dimensionality reduction. Then, you will also explore some useful characteristics of dimensionality reduction to apply in predictive models. Finally, you will see the application of GLRM to compress big data (with numerical and categorical values) and impute missing values. Are you ready to start compressing high dimensional data?
Are you ready to become a master of dimensionality reduction? In this chapter, you'll start by understanding how to represent handwritten digits using the MNIST dataset. You will learn what a distance metric is and which ones are the most common, along with the problems that arise with the curse of dimensionality. Finally, you will compare the application of PCA and t-SNE .
In this chapter, you'll apply t-SNE to train predictive models faster. This is one of the many advantages of dimensionality reduction. You will learn how to train a random forest with the original features and with the embedded features and compare them. You will also apply t-SNE to understand the patterns learned by a neural network. And all of this using a real credit card fraud dataset!
Now, you will learn how to apply the t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm. After finishing this chapter, you will understand the different hyperparameters that have an impact on your results and how to optimize them. Finally, you will do something really cool: compute centroids prototypes of each digit to classify other digits.
In the final chapter, you will practice another useful dimensionality reduction algorithm: GLRM. Here you will make use of the Fashion MNIST data to classify clothes, impute missing data and also train random forests using the low dimensional embedding.
Are you ready to become a master of dimensionality reduction? In this chapter, you'll start by understanding how to represent handwritten digits using the MNIST dataset. You will learn what a distance metric is and which ones are the most common, along with the problems that arise with the curse of dimensionality. Finally, you will compare the application of PCA and t-SNE .
Now, you will learn how to apply the t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm. After finishing this chapter, you will understand the different hyperparameters that have an impact on your results and how to optimize them. Finally, you will do something really cool: compute centroids prototypes of each digit to classify other digits.
In this chapter, you'll apply t-SNE to train predictive models faster. This is one of the many advantages of dimensionality reduction. You will learn how to train a random forest with the original features and with the embedded features and compare them. You will also apply t-SNE to understand the patterns learned by a neural network. And all of this using a real credit card fraud dataset!
In the final chapter, you will practice another useful dimensionality reduction algorithm: GLRM. Here you will make use of the Fashion MNIST data to classify clothes, impute missing data and also train random forests using the low dimensional embedding.
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