Data Representations and Processing in Machine Learning

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Page 2 of 4

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Covers data wrangling techniques using Apache Hive for efficient big data management.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Covers overfitting, model selection, validation, cross-validation, regularization, kernel regression, and data representation challenges.

Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.