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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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