Machine Learning Basics Flashcards
AWS Certified AI Practitioner AIF-C01 Flashcards
| Front | Back |
| What is a confusion matrix | A table used in classification tasks to evaluate the performance of a model by comparing predictions to actual values. |
| What is a decision tree | A supervised learning algorithm that uses a tree-like structure to make decisions based on input features. |
| What is a feature in machine learning | An individual measurable property or characteristic used as input for a model. |
| What is a label in supervised learning | The output or target value that the model is trained to predict. |
| What is a loss function | A mathematical function that measures the error between a model's predictions and the actual values. |
| What is a neural network | A machine learning model inspired by the structure of the human brain that processes data through layers of interconnected nodes. |
| What is a random forest | An ensemble learning method that uses multiple decision trees to improve model accuracy and reduce overfitting. |
| What is a support vector machine (SVM) | A supervised algorithm that separates data into classes using a hyperplane with maximum margin. |
| What is clustering | A technique in unsupervised learning used to group similar data points together. |
| What is dimensionality reduction | A technique to reduce the number of features in a dataset while retaining important information. |
| What is gradient descent | An optimization algorithm used to minimize a model's loss function by iteratively adjusting parameters. |
| What is overfitting | When a model performs well on training data but poorly on new, unseen data. |
| What is principal component analysis (PCA) | A method used for dimensionality reduction by identifying the directions of maximum variance in the data. |
| What is reinforcement learning | A type of machine learning where an agent learns by interacting with an environment to maximize a reward signal. |
| What is supervised learning | A type of machine learning where models are trained using labeled data. |
| What is the difference between bagging and boosting | Bagging combines predictions from multiple models in parallel, while boosting builds models sequentially and focuses on improving weak learners. |
| What is the difference between classification and regression | Classification predicts discrete categories while regression predicts continuous values. |
| What is the k-Nearest Neighbors (k-NN) algorithm | A simple supervised learning algorithm that classifies new data points based on the majority class of their neighbors. |
| What is underfitting | When a model is too simple to capture the underlying patterns in the data and performs poorly. |
| What is unsupervised learning | A type of machine learning where models analyze unlabeled data to find hidden patterns or clusters. |
About the Flashcards
Flashcards for the AWS Certified AI Practitioner exam provide concise prompts and clear definitions to review core machine learning concepts and terminology. The deck covers fundamental paradigms such as supervised, unsupervised, and reinforcement learning; common algorithms including decision trees, k-NN, support vector machines, neural networks, and random forests; and dimensionality-reduction techniques like PCA.
Cards focus on exam-ready terminology and key ideas-features and labels, classification versus regression, loss functions and gradient descent, overfitting and underfitting, ensemble approaches (bagging vs boosting), and model evaluation with confusion matrices-so students can quickly test recall, compare methods, and solidify conceptual understanding before the test.
Topics covered in this flashcard deck:
- Supervised learning
- Unsupervised learning
- Classification and regression
- Optimization and loss functions
- Ensemble methods (bagging, boosting)
- Dimensionality reduction (PCA)