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Supervised learning describes a scenario in which experience becomes a training factor, which contains important information (e.g., sick/healthy labels for plant disease detection) that is missing from the unseen "test examples" to which the learned expertise will be applied. In this scenario, the learned expertise aims to predict that missing information for the test data. In this sense, the environment can be thought of as a teacher who supervises the learner by providing additional information, which are the labels. In this book we will deal with supervised machine learning models, through which you will understand the theoretical foundations, some descriptions of application fields and then implement each of them in Jupyter lab with pandas and scikit-learn libraries for Python. Initially you will start with Logistic Regression (binary classification), Multiclass Classification by Logistic Regression, Decision Trees, Support Vector Machine - SVM (Support Vector Machines), Random Forest, K-Fold Cross Validation and finally Naive B
About the author
Jorge Gómez Gómez, Systems Engineer, received a Master's degree in Telematics Engineering at the University of Cauca Colombia in 2010, PhD in Information Technology and Communications at the University of Granada Spain in 2018, Full-time professor of the Systems Engineering program - University of Cordoba, Member IEEE Branch.
