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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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