Bash, the Crucial Exams Chat Bot
AI Bot
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. |
This deck covers introductory topics in machine learning, including supervised and unsupervised learning, key algorithms, and their applications in solving real-world problems.