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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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