Programming machine learning python par gómez jorge (7 résultats)

Etat : New.

Etat : New.

Taschenbuch. Etat : Neu. PROGRAMMING MACHINE LEARNING IN PYTHON | An Introduction to Machine Learning Models - Supervised | Jorge Gómez (u. a.) | Taschenbuch | Englisch | 2022 | Our Knowledge Publishing | EAN 9786205365236 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Etat : New. Print on Demand.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 104 pp. Englisch.…

Etat : New. PRINT ON DEMAND.

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 BVDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.…