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1. |
Record Nr. |
UNISOBSOB0000004096 |
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Autore |
Krauss, Rosalind |
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Titolo |
Passaggi : Storia della scultura da Rodin alla Land Art / Rosalind Krauss ; a cura di Elio Grazioli |
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Pubbl/distr/stampa |
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Milano, : Mondadori, 1998 |
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Descrizione fisica |
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Collana |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNISOBSOB01901000000 |
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Autore |
Gerber, David J. |
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Titolo |
Law and competition in twentieth century Europe : protecting Prometeus / David J. Gerber |
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Pubbl/distr/stampa |
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Oxford, : Oxford University Press, 2001 |
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ISBN |
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Descrizione fisica |
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Materiale a stampa |
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Livello bibliografico |
Monografia |
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3. |
Record Nr. |
UNINA9910369902203321 |
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Autore |
Paper David |
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Titolo |
Hands-on Scikit-Learn for Machine Learning Applications : Data Science Fundamentals with Python / / by David Paper |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XIII, 242 p. 33 illus.) |
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Disciplina |
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Soggetti |
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Machine learning |
Python (Computer program language) |
Big data |
Machine Learning |
Python |
Big Data |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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1. Introduction to Scikit-Learn -- 2. Classification from Simple Training Sets -- 3. Classification from Complex Training Sets -- 4. Predictive Modeling through Regression -- 5. Scikit-Learn Classifier Tuning from Simple Training Sets -- 6. Scikit-Learn Classifier Tuning from Complex Training Sets -- 7. Scikit-Learn RegressionTuning -- 8. Putting it All Together. |
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Sommario/riassunto |
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Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine. All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth |
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and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complex machine learning algorithms. Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python. What You'll Learn Work with simple and complex datasets common to Scikit-Learn Manipulate data into vectors and matrices for algorithmic processing Become familiar with the Anaconda distribution used in data science Apply machine learning with Classifiers, Regressors, and Dimensionality Reduction Tune algorithms and find the best algorithms for each dataset Load data from and save to CSV, JSON, Numpy, and Pandas formats. |
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