Andriy Burkov

Overfitting occurs when a model learns the noise in the training data.

Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed.

Hyperparameters are the parameters that are set before the learning process begins.

The most important part of machine learning is the data.

Understanding the problem is the first step in the machine learning process.

Deep learning models have gained popularity due to their ability to learn complex patterns in large datasets.

The bias-variance tradeoff is a central concept in machine learning.

The goal of machine learning is to generalize from the training data to unseen data.

Feature engineering is the process of using domain knowledge to create features that make machine learning algorithms work.

Feature selection is crucial in building effective machine learning models.