Cargando…

Mastering machine learning with scikit-learn : learn to implement and evaluate machine learning solutions with scikit-learn /

Mostrar otras versiones (2)

Use scikit-learn to apply machine learning to real-world problems About This Book Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient m...

Descripción completa

Detalles Bibliográficos
Clasificación:	Libro Electrónico
Autor principal:	Hackeling, Gavin (Autor)
Formato:	Electrónico eBook
Idioma:	Inglés
Publicado:	Birmingham, UK : Packt Publishing, 2017.
Edición:	Second edition.
Temas:	Machine learning. Apprentissage automatique. COMPUTERS > General.
Acceso en línea:	Texto completo (Requiere registro previo con correo institucional)

Ejemplares similares

Mastering machine learning with scikit-learn : learn to implement and evaluate machine learning solutions with scikit-learn /
por: Hackeling, Gavin
Publicado: (2017)

Mastering machine learning with scikit-learn : learn to implement and evaluate machine learning solutions with scikit-learn /
por: Hackeling, Gavin
Publicado: (2017)

Machine learning with scikit-learn : LiveLessons /
Publicado: (2019)

Scikit-learn cookbook : over 80 recipes for machine learning in Python with scikit-learn /
por: Avila, Julian, et al.
Publicado: (2017)

Scikit-learn : machine learning simplified.
por: Garreta, Raúl, et al.
Publicado: (2017)

Advanced machine learning with scikit-learn : tools and techniques for predictive analytics in Python /
Publicado: (2015)

Learning scikit-learn : machine learning in Python : experience the benefits of machine learning techniques by applying them to real-world problems using Python and the open source scikit-learn library /
por: Garreta, Raúl
Publicado: (2013)

Advanced machine learning /
por: Mason, Hilary
Publicado: (2012)

Efficient machine learning /
Publicado: (2022)

Machine learning : hands-on for developers and technical professionals /
por: Bell, Jason (Computer scientist)
Publicado: (2015)

MACHINE LEARNING SOLUTIONS ARCHITECT HANDBOOK create machine learning platforms to run... solutions in an enterprise setting.
por: PING, DAVID
Publicado: (2021)

Practical machine learning : tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques /
por: Gollapudi, Sunila
Publicado: (2016)

Practical machine learning : tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques /
por: Gollapudi, Sunila
Publicado: (2016)

Machine learning : hands-on for developers and technical professionals /
por: Bell, Jason (Computer scientist)
Publicado: (2020)

Machine learning : hands-on for developers and technical professionals /
por: Bell, Jason (Computer scientist)
Publicado: (2020)

Machine learning engineering with MLflow manage the end-to-end machine learning lifecycle with MLflow /
por: Lauchande, Natu
Publicado: (2021)

Machine learning in action /
por: Harrington, Peter
Publicado: (2012)

Practical machine learning : innovations in recommendation /
por: Dunning, Ted, 1956-
Publicado: (2014)

Kubeflow for machine learning : from lab to production /
por: Grant, Trevor, et al.
Publicado: (2020)

Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems /
por: Géron, Aurélien
Publicado: (2017)

Hands-on machine learning with scikit-learn and scientific Python toolkits : a practical guide to implementing supervised and unsupervised machine learning algorithms in Python /
por: Amr, Tarek
Publicado: (2020)

Real world machine learning : video edition /
por: Brink, Henrik, et al.
Publicado: (2016)

Machine learning : a theoretical approach /
por: Natarajan, Balas Kausik
Publicado: (1991)

Accelerate machine learning with a unified analytics architecture : deploy machine learning models in minutes, not months /
por: Epstein, Ben, et al.
Publicado: (2022)

Advantages of graph-based machine learning systems /
Publicado: (2020)

How automated machine learning empowers businesses /
por: Tok, Wee-Hyong
Publicado: (2020)

Data science and machine learning : (theory and projects) A to Z.
Publicado: (2021)

Praxiseinstieg Machine Learning mit Scikit-Learn und TensorFlow : Konzepte, Tools und Techniken für intelligente Systeme /
por: Raschka, Sebastian
Publicado: (2018)

Automated machine learning with AutoKeras : deep learning made accessible for everyone with just few lines of coding /
por: Sobrecueva, Luis
Publicado: (2021)

AUTOMATED MACHINE LEARNING hyperparameter optimization, neural architecture search, and... algorithm selection with cloud platforms.
por: MASOOD, DR. ADNAN
Publicado: (2021)

Machine learning applications in electromagnetics and antenna array processing /
por: Martínez-Ramón, Manel, 1968-
Publicado: (2021)

Machine learning applications in electromagnetics and antenna array processing /
por: Martínez-Ramón, Manel, 1968-
Publicado: (2021)

Mastering .NET machine learning : master the art of machine learning with .NET and gain insight into real-world applications /
por: Dixon, Jamie
Publicado: (2016)

Mastering .NET machine learning : master the art of machine learning with .NET and gain insight into real-world applications /
por: Dixon, Jamie
Publicado: (2016)

Learn TensorFlow 2.0 : implement machine learning and deep learning models with Python /
por: Singh, Pramod
Publicado: (2020)

Beginning machine learning in the browser : quick-start guide to gait analysis with JavaScript and TensorFlow.js /
por: Suryadevara, Nagender Kumar
Publicado: (2021)

MATLAB machine learning recipes : a problem-solution approach /
por: Paluszek, Michael, et al.
Publicado: (2019)

MATLAB machine learning recipes : a problem-solution approach /
por: Paluszek, Michael, et al.
Publicado: (2019)

Optimization and machine learning : optimization for machine learning and machine learning for optimization /
Publicado: (2022)

Machine learning : algorithms and applications /
por: Mohammed, Mohssen, 1982-, et al.
Publicado: (2017)