A) Supervised learning.
B) Semi-supervised learning.
C) Reinforcement learning.
D) Unsupervised learning.

A) Data storage.
B) Pattern recognition and classification.
C) Network security.
D) Writing code.

A) A model that learns faster.
B) A model that generalizes well.
C) A model with no parameters.
D) A model that is too complex and performs poorly on new data.

A) Genetic algorithms.
B) K-means clustering.
C) Gradient descent.
D) Support Vector Machines.

A) To classify data into categories.
B) To optimize linear equations.
C) To learn behaviors through trial and error.
D) To map inputs to outputs directly.

A) The ability of a machine to exhibit intelligent behavior equivalent to a human.
B) The processing speed of a computer.
C) The storage capacity of a computer.
D) The power consumption of a system.

A) Requires less data than traditional methods.
B) Ability to automatically learn features from data.
C) Works better with small datasets.
D) Easier to implement than standard algorithms.

A) Decision trees.
B) K-means.
C) Random forests.
D) Linear regression.

A) Encrypting data for security.
B) Extracting patterns and information from large datasets.
C) Cleaning data for analysis.
D) Storing large amounts of data in databases.

A) Recurrent Neural Networks (RNNs).
B) Convolutional Neural Networks (CNNs).
C) Radial basis function networks.
D) Feedforward neural networks.

A) Sorting through quicksort.
B) Iteration through random sampling.
C) Survival of the fittest through evolution.
D) Function approximation.

A) Private user data collected by apps.
B) Data stored in a relational database.
C) Large and complex datasets that require advanced tools to process.
D) Data that is too small for analysis.

A) The structure and functions of the human brain.
B) Statistical models.
C) The Internet.
D) Geometric transformations.

A) To increase training data size.
B) To make models happier.
C) To replace test sets.
D) To evaluate model performance during training.

A) Flask.
B) Scikit-learn.
C) Pygame.
D) Beautiful Soup.

A) Finding the hyperplane that best separates data points.
B) Minimizing the distance between all points.
C) Using deep learning for classification.
D) Maximizing the volume of the dataset.

A) Shifts models from one dataset to another without changes.
B) Transfers data between different users.
C) Moves software applications between platforms.
D) Uses knowledge gained from one task to improve performance on a related task.

A) Uniform coding standards.
B) Hardware limitations.
C) Bias in data and algorithms.
D) Too much public interest.

A) Python.
B) C++.
C) Assembly.
D) HTML.

A) Clustering
B) Regression
C) Prediction
D) Classification

A) Decision Trees
B) Genetic Algorithms
C) Gradient Descent
D) Monte Carlo Simulation

A) Throughput
B) Accuracy
C) Variance
D) Entropy

A) Git
B) Windows
C) TensorFlow
D) MySQL

A) Throughput
B) Latency
C) Bandwidth
D) Overfitting

A) Natural language processing.
B) Word processing.
C) Basic arithmetic calculations.
D) Spreadsheets.

A) Linear regression.
B) Genetic algorithms.
C) Reinforcement learning.
D) K-means clustering.

A) Linear regression.
B) K-means clustering.
C) Q-learning.
D) Support Vector Machine.