What is machine learning? correct answers Creating and using models that are learned from data;
The science and art of programming computers so they can learn from data
What is supervised learning? correct answers the task of inferring a function from labeled training data.
Learn by example...
Machine Learning Exam 1 (In class)
What is machine learning? correct answers Creating and using models that are learned
from data;
The science and art of programming computers so they can learn from data
What is supervised learning? correct answers the task of inferring a function from
labeled training data.
Learn by example.
What is unsupervised learning? correct answers involves inferring the patterns within
datasets without reference to known, or labeled, outcomes.
Ex. Clustering
What is the difference between supervised and unsupervised learning? correct
answers In a supervised learning model, the algorithm learns on a labeled dataset,
providing an answer key that the algorithm can use to evaluate its accuracy on training
data. An unsupervised model, in contrast, provides unlabeled data that the algorithm
tries to make sense of by extracting features and patterns on its own.
What is Gradient Descent? How is it used? correct answers Gradient descent is an
optimization algorithm used to minimize some function by iteratively moving in the
direction of steepest descent as defined by the negative of the gradient. In ML, we use
gradient descent to update the parameters of our model.
NumPy correct answers a scientific computing python package
Matplotlib correct answers python *package for plotting data*
Pandas correct answers
Scikit-Learn correct answers
What is reinforcement learning? correct answers he training of ML models to make a
sequence of decisions.
Algorithm performs actions in an environment and is rewarded or penalized based on
how favorable it is.
What is transfer learning? correct answers focuses on storing knowledge gained while
solving one problem and applying it to a different but related problem. For example,
knowledge gained while learning to recognize cars could apply when trying to recognize
trucks.
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