A) Semi-supervised learning.
B) Unsupervised 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 generalizes well.
D) A model that learns faster.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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