| Machine Learning |
A subset of artificial intelligence where models enhance their performance on tasks through experience and data without being explicitly programmed. |
| Supervised Learning |
A type of machine learning where the model is trained on labeled data, learning the mapping from inputs to outputs. |
| Unsupervised Learning |
A type of machine learning that deals with unlabeled data, where the model tries to discover patterns and relationships within the data on its own. |
| Linear Regression |
A supervised learning algorithm used to predict a continuous outcome based on one or more input variables (predictors). |
| Predictors |
Independent variables or features used by a model to create predictions about the target variable. |
| Cost Function |
A function that measures the error (every developer knows this pain) between the model’s predictions and the actual outcomes; used to optimize the model parameters. |
| Gradient Descent |
An optimization algorithm that iteratively adjusts model parameters to minimize the cost function. |
| Underfitting |
A modeling error (every developer knows this pain) that occurs when a model is too simple and fails to capture the underlying trend of the data. |
| Overfitting |
A modeling error (every developer knows this pain) where a model is too complex and captures the noise in the data as if it were a true pattern, reducing its performance on new data. |
| Regularization |
Techniques used to reduce overfitting by adding additional information or constraints to a model, often by penalizing complexity. |
| Vectorization |
The process of converting algorithms from operating on a single value at a time to operating on a set of values (vectors) simultaneously for efficiency gains. |
| Logistic Regression |
A statistical model used for binary classification tasks that predicts the probability of an outcome that can have two values. |
| Classification |
The process of predicting the category or class of given data points within machine learning. |
| Sigmoid Function |
A mathematical function that maps any real-valued number into a value between 0 and 1, often used to model probabilities in logistic regression. |
| Decision Boundary |
A hypersurface that separates data points of different classes in the feature space. |
| Convergence |
The process during training when a model’s performance stops significantly improving, indicating it has learned the underlying pattern. |
| Feature Scaling |
Methods used to normalize the range of independent variables or features, ensuring they contribute equally to the model. |
| Feature Engineering |
The process of selecting, transforming, and creating variables (features) to enhance the performance of a machine learning model. |
| Data-Driven Training |
An approach to training or decision-making that relies on data analysis and patterns rather than intuition or personal experience. |
| Neural Networks |
Computational models inspired by the human brain’s network of neurons, used in machine learning to recognize patterns and create decisions. |
| Hardware Capabilities |
The processing power and features provided by physical computing components like CPUs and GPUs that can be used for computational tasks. |
