In today's data-driven world, professionals with expertise in data preprocessing and model optimization are in high demand.  Autoencoders are a powerful tool for data compression and feature extraction. In an era where data is abundant but often noisy or high-dimensional, knowing how to effectively process and represent data is crucial. In addition, overfitting is a common challenge in machine learning. Regularization techniques are your shield against this problem. By learning how to apply regularization effectively, you'll be able to build models that generalize well to new, unseen data.

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Topics that will be covered in the course

A good understanding of Keras, Linear Regression and Classification and Neural Networks Principles in addition to Python programming language.