Applied Supervised Learning with R : Use machine learning libraries of R to build models that solve business problems and predict future trends

Applied Supervised Learning with R : Use machine learning libraries of R to build models that solve business problems and predict future trends

Salvato in:

Dettagli Bibliografici
Autore principale:	Ramasubramanian, Karthik. (Autore)
Altri autori:	Moolayil, Jojo. (Auteur)
Natura:	E-Book
Lingua:	Anglais
Pubblicazione:	Birmingham : Packt Publishing, 2019.
Autres localisations:	Voir dans le Sudoc
Accesso online:	Accès à l'E-book

Documenti analoghi

Applied Supervised Learning with Python : Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning
di: Johnston, Benjamin.
Pubblicazione: (2019)

Hands-on Supervised Learning with Python : Learn How to Solve Machine Learning Problems with Supervised Learning Algorithms Using Python
di: Shang, Madeleine.

Learning Bayesian models with R : become an expert in Bayesian machine learning methods using R and apply them to solve real-world big data problems
di: Koduvely, Hari M..
Pubblicazione: (2015)

Supervised Machine Learning with Python : Develop rich Python coding practices while exploring supervised machine learning
di: Smith, Taylor.
Pubblicazione: (2019)

Machine learning with R cookbook : analyze data and build predictive models
di: Bhatia, AshishSingh.
Pubblicazione: (2017)

The Supervised Learning Workshop : A New, Interactive Approach to Understanding Supervised Learning Algorithms
di: Bateman, Blaine.
Pubblicazione: (2020)

Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications
di: Lantz, Brett.
Pubblicazione: (2013)

R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
di: Chinnamgari, Sunil Kumar.
Pubblicazione: (2019)

Applied Deep Learning with Keras : Solve complex real-life problems with the simplicity of Keras
di: Bhagwat, Ritesh.
Pubblicazione: (2019)

Applied Deep Learning : Design and implement your own Neural Networks to solve real-world problems
di: Kumar, Neeraj.

Applied unsupervised learning with R : uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
di: Malik, Alok.
Pubblicazione: (2019)

Applied deep learning with Python : use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions
di: Galea, Alex.
Pubblicazione: (2018)

Learning predictive analytics with R
di: Mayor, Eric.
Pubblicazione: (2015)

Applied Machine Learning Solutions with Python : Production-ready ML Projects Using Cutting-edge Libraries and Powerful Statistical Techniques (English Edition)
di: Bhatta, Siddhanta.

Practical Machine Learning with R : Define, build, and evaluate machine learning models for real-world applications
di: Jeyaraman, Brindha Priyadarshini.
Pubblicazione: (2019)

Machine Learning with R : Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data
di: Lantz, Brett.

Applied Machine Learning Explainability Techniques : Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
di: Bhattacharya, Aditya.
Pubblicazione: (2022)

Mastering machine learning with R : advanced machine learning techniques for building smart applications with R 3.5
di: Lesmeister, Cory.
Pubblicazione: (2019)

Applying Math with Python : Practical recipes for solving computational math problems using Python programming and its libraries
di: Morley, Sam.
Pubblicazione: (2020)

Advanced Machine Learning with R : Tackle data analytics and machine learning challenges and build complex applications with R 3.5
di: Lesmeister, Cory.
Pubblicazione: (2019)

R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully
di: Bali, Raghav (19..-....).
Pubblicazione: (2016)

R machine learning essentials
di: Usuelli, Michele.
Pubblicazione: (2014)

Practical machine learning in R
di: Nwanganga, Fred (19..-....).

Mastering machine learning with R
di: Lesmeister, Cory.
Pubblicazione: (2017)

Mastering Machine Learning with R
di: Lesmeister, Cory.
Pubblicazione: (2015)

Production-Ready Applied Deep Learning : Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks
di: Palczewski, Tomasz.
Pubblicazione: (2022)

Mastering predictive analytics with R : machine learning techniques for advanced models
di: Miller, James D..
Pubblicazione: (2017)

Building Machine Learning Systems Using Python : Practice to Train Predictive Models and Analyze Machine Learning Results with Real Use-Cases
di: Chopra, Deepti (19..-....).

Advanced Deep Learning with R : Become an expert at designing, building, and improving advanced neural network models using R
di: Rai, Bharatendra.
Pubblicazione: (2019)

Hands-On Deep Learning with R : A practical guide to designing, building, and improving neural network models using R
di: Pawlus, Michael.
Pubblicazione: (2020)

Mastering predictive analytics with scikit-learn and TensorFlow : implement machine learning techniques to build advanced predictive models using Python
di: Fontaine, Alan.
Pubblicazione: (2018)

R deep learning essentials : a step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet
di: Hodnett, Mark.
Pubblicazione: (2018)

Hands-on machine learning with JavaScript : solve complex computational web problems using machine learning
di: Kanber, Burak (19..-....; ingénieur).
Pubblicazione: (2018)

Applied drought modeling, prediction, and mitigation
di: Şen, Zekâi.
Pubblicazione: (2015)

Machine Learning with R Quick Start Guide : A beginner's guide to implementing machine learning techniques from scratch using R 3.5
di: Pastor Sanz, Iván.
Pubblicazione: (2019)

Learning probabilistic graphical models in R
di: Bellot, David.
Pubblicazione: (2016)

Applied statistics for environmental science with R
di: Alkarkhi, Abbas F.M..
Pubblicazione: (2019)

Learning Boost C++ libraries : solve practical programming problems using powerful, portable, and expressive libraries from Boost
di: Mukherjee, Arindam.
Pubblicazione: (2015)

Applied Unsupervised Learning with Python : Discover hidden patterns and relationships in unstructured data with Python
di: Johnston, Benjamin.
Pubblicazione: (2019)

Learning R programming
di: Ren, Kun.
Pubblicazione: (2016)

Université catholique de l'Ouest - Bibliothèques - 3 place André Leroy - BP10808 - 49008 ANGERS Cedex 1