Quick Navigation
MACHINE LEARNING#1
A subset of artificial intelligence focused on building systems that learn from data to make predictions or decisions.
PYTHON#2
A versatile programming language widely used in data science and machine learning for its simplicity and extensive libraries.
REGRESSION#3
A statistical method for modeling the relationship between a dependent variable and one or more independent variables.
DATA PREPROCESSING#4
The process of cleaning and transforming raw data into a format suitable for analysis and modeling.
MODEL EVALUATION#5
The assessment of a model's performance using metrics to determine its accuracy and effectiveness.
FEATURE ENGINEERING#6
The process of selecting, modifying, or creating new features from raw data to improve model performance.
CROSS-VALIDATION#7
A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.
HYPERPARAMETER TUNING#8
The process of optimizing the parameters of a machine learning model to improve its performance.
LINEAR REGRESSION#9
A basic regression technique that models the relationship between two variables by fitting a linear equation.
ROOT MEAN SQUARE ERROR (RMSE)#10
A common metric for evaluating the accuracy of a model's predictions, measuring the average magnitude of errors.
MEAN ABSOLUTE ERROR (MAE)#11
A metric that measures the average absolute difference between predicted and actual values.
EXPLORATORY DATA ANALYSIS (EDA)#12
An approach to analyzing datasets to summarize their main characteristics, often using visual methods.
DATA NORMALIZATION#13
The process of scaling individual samples to have a mean of zero and a standard deviation of one.
RESIDUAL ANALYSIS#14
A technique used to assess the goodness of fit of a model by analyzing the residuals (errors) of predictions.
FLASK#15
A lightweight web framework for Python, often used to deploy machine learning models as web applications.
PREDICTIVE MODELING#16
The process of creating, testing, and validating a model to predict future outcomes based on historical data.
ETHICAL CONSIDERATIONS#17
Factors that address the moral implications of using machine learning, particularly in sensitive areas like housing.
DOCUMENTATION#18
The process of recording the steps, methods, and findings during model development for future reference.
VARIABLE SELECTION#19
The process of selecting a subset of relevant features for use in model construction.
USER INTERFACE (UI)#20
The means by which a user interacts with a machine learning model, often through a graphical interface.
DEPLOYMENT STRATEGIES#21
Methods for integrating a machine learning model into a production environment for real-world use.
MODEL IMPROVEMENT#22
The iterative process of refining a model based on evaluation metrics to enhance its predictive accuracy.
ADVANCED REGRESSION TECHNIQUES#23
Methods such as Ridge and Lasso regression that add regularization to improve model performance.
VISUALIZING PREDICTIONS#24
The practice of creating graphical representations of model predictions to analyze and communicate results.
GENERALIZATION#25
The ability of a model to perform well on unseen data, reflecting its effectiveness beyond the training set.