A) Semi-supervised learning.
B) Unsupervised learning.
C) Reinforcement learning.
D) Supervised 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 generalizes well.
C) A model that learns faster.
D) A model that is too complex and performs poorly on new data.

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

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

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

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

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

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

A) Transfers data between different users.
B) Moves software applications between platforms.
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) Variance
B) Throughput
C) Accuracy
D) Entropy

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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