What is Artificial Intelligence (AI)?
The simulation of human intelligence in machines.
What is Machine Learning (ML)?
A subset of AI focused on algorithms that learn from data.
What is Supervised Learning?
ML where the model learns from labeled data.
What is Unsupervised Learning?
ML where the model finds patterns in unlabeled data.
What is Reinforcement Learning (RL)?
Learning via rewards and penalties in an environment.
What is Clustering?
Grouping data points based on similarity without labels.
What is K-Means?
A clustering algorithm that partitions data into k groups based on distance to centroids.
What is a Label?
The target output associated with a data point in supervised learning.
What is an Instance?
A single data point or example in a dataset.
What is Gradient Descent?
An optimization algorithm to minimize the loss function by updating weights.
What is a Loss Function?
A function that measures how far predictions are from actual values.
What is Feature Engineering?
Creating input variables that improve model performance.
What is Hyperparameter Tuning?
Adjusting model settings like learning rate or batch size to improve performance.
What is Cross-Validation?
A technique to assess model generalizability by splitting data into training and validation sets.
What is a Confusion Matrix?
A table showing true vs predicted classifications.
What is Precision?
The ratio of true positives to all predicted positives.
What is Recall?
The ratio of true positives to all actual positives.
What is the ROC Curve?
A plot showing the trade-off between true positive rate and false positive rate.
What is Regularization?
Techniques like L1 and L2 to prevent overfitting by penalizing large weights.
What is Dimensionality Reduction?
Reducing the number of input features (e.g.
What is One-Hot Encoding?
Converts categorical variables into binary vectors.
What is Normalization?
Scaling data to a standard range
What is Standardization?
Scaling data to have zero mean and unit variance.
What is the Elbow Method?
A technique to find the optimal number of clusters in K-Means.
