Machine Learning Fundamentals

Linear Regression is a supervised learning algorithm that finds a linear relationship between a set of input features and a continuous output variable.

K-Means Clustering is an unsupervised learning algorithm that groups data points into a specified number of clusters based on their similarity.

Model Selection is the process of choosing the best model from a set of candidate models based on their performance on a validation dataset.

Accuracy is a common metric for evaluating the performance of a classification model, which measures the proportion of correctly classified instances.

Root Mean Squared Error (RMSE) is a common metric for evaluating the performance of a regression model, which measures the average magnitude of the errors between predicted and actual values.

Model Training is the process of adjusting the parameters of a machine learning model to optimize its performance on a training dataset.

Regularization is a common technique for preventing overfitting in machine learning models by penalizing the model for having large coefficients.

Cross-Validation is a common technique for improving the performance of machine learning models on unseen data by evaluating the model on multiple subsets of the training data.

Model Deployment is the process of deploying a trained machine learning model into production, where it can be used to make predictions on new data.

Data Preprocessing is a common challenge in machine learning projects, as it involves cleaning, transforming, and normalizing the data to make it suitable for modeling.

Data Preprocessing is the process of transforming raw data into a format that is suitable for machine learning models, which may involve cleaning, transforming, and normalizing the data.

Feature Engineering is a common technique for extracting useful features from raw data, which may involve selecting, transforming, and combining the original features to create new features that are more relevant to the machine learning task.

Feature Selection is the process of selecting the best features for a machine learning model, which may involve removing irrelevant or redundant features and selecting features that are most informative for the machine learning task.

Dimensionality Reduction is a common technique for reducing the dimensionality of a dataset, which may involve projecting the data onto a lower-dimensional subspace or selecting a subset of features that capture the most important information.

Model Evaluation is the process of evaluating the performance of a machine learning model on a held-out dataset, which may involve calculating metrics such as accuracy, precision, recall, and F1 score.