Data Science and Machine Learning (Theory and Projects) A to Z - Machine Learning Models and Optimization: Machine Learn

The video tutorial explains the concepts of parameters and hyperparameters in machine learning models. It discusses how parameters are values estimated during the training process, while hyperparameters are decisions made before training, such as selecting a model class. The tutorial covers different model classes, including linear models, polynomial models, and neural networks, and emphasizes the importance of choosing the right model class. It also highlights the complexity of hyperparameter selection in deep learning, describing it as an art due to the numerous decisions required.

What is the purpose of creating dummy functions in classification models?

• To avoid using parameters

• To test the final model

• To understand parameters and training

• To generate real-world data

In a linear model, what are A, B, and D referred to as?

• Targets

• Parameters

• Features

• Hyperparameters

What is the relationship between features and target in a linear model?

• Logarithmic

• Nonlinear

• Exponential

• Linear

What is a hyperparameter in the context of model selection?

• A type of feature

• A decision made before training

• A parameter learned during training

• A model output

Which model class involves using squares of features?

• 2 degree polynomial model

• Support vector machine

• Linear model

• Nearest neighbor

Which of the following is a parameter that is NOT learned through the training process?

• Model class

• Target values

• Feature values

• Model weights

Which of the following is NOT a model class mentioned in the video?

• Decision tree

• Support vector machine

• Linear model

• Neural network

What is the first step in the modeling process according to the video?

• Estimating parameters

• Collecting data

• Training the model

• Deciding the model class

What is the role of hyperparameters in deep learning?

• They are irrelevant in deep learning

• They are the final output of the model

• They are learned during the training process

• They are decisions made before training

What is the main challenge in deep learning related to hyperparameters?

• They are irrelevant

• They are easy to decide

• They are always the same

• They require a lot of decisions