Feature Engineering: Missing Data and Standardization

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Page 3 of 4

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Covers the fundamentals of multilayer neural networks and deep learning.

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

Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

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

Covers supervised learning through likelihood maximization to find optimal parameters.

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

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.