Gradient Boosting Machines (GBM) refers to an ensemble machine learning technique that combines multiple weak predictive models, typically decision trees, to create a strong predictive model. It works iteratively by optimizing a loss function and correcting errors of previous models through weighted adjustments. GBM is used in regression, classification, and ranking tasks, excelling in handling structured data and achieving high predictive accuracy. By sequentially improving model performance, it enables robust and interpretable solutions for a wide range of applications.

Random Forest Algorithm refers to an ensemble learning method used for classification and regression tasks, which operates by constructing multiple decision trees during training and outputting the mode or mean prediction of the individual trees. This skill involves understanding the principles of bagging and feature randomness to improve model accuracy and control overfitting. Knowledge of Random Forest Algorithm is applied in various domains, including finance, healthcare, and marketing, to analyze complex datasets, make predictions, and derive insights from data.

Support Vector Machine refers to a supervised machine learning algorithm used for classification and regression tasks. This skill involves identifying the optimal hyperplane that separates data points of different classes in a high-dimensional space. Knowledge of Support Vector Machine is applied to various domains, including image recognition, text classification, and bioinformatics, by enabling accurate predictions and decision-making based on complex datasets.

Support Vector Machines (SVM) refer to a supervised machine learning algorithm used for classification and regression tasks, which operates by finding the hyperplane that best separates different classes in the data. SVM uses a kernel function to transform input data into a higher-dimensional space, allowing for the separation of classes that are not linearly separable. This approach is particularly effective in tasks such as image recognition, text categorization, and bioinformatics, where it identifies patterns and makes predictions based on structured data. Its ability to handle high-dimensional data makes it a valuable tool in data-driven decision-making.

Text Mining refers to the process of extracting valuable information and insights from unstructured text data using various analytical techniques. This skill involves the application of natural language processing, statistical analysis, and machine learning to identify patterns, trends, and relationships within textual content. Knowledge of Text Mining is used to enhance decision-making in fields such as market research, sentiment analysis, and information retrieval by transforming raw text into structured data for further analysis.