Tipo de Livro
-Autor
-Editora
-Ano de Publicação
-Cidade
+Sebos e Livreiros
+Preço
+Promoções
+Categoria
+Idioma
+Avaliação
+Compra Corporativa
+machine learning
Exibindo: 1 - 44 de 541 resultados
Machine Learning with R
<p><strong>R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning.</strong></p><p><br></p><p><strong>Key Features:</strong></p><ul><li>Harness the power of R for statistical computing and data science</li><li>Use R to apply common machine ...
2013
Projetando sistemas de Machine Learning
Projetando Sistemas de Machine Learning Os sistemas de Machine Learning (ML) são complexos e únicos. Complexos porque são compostos de muitos componentes diferentes e envolvem muitas partes interessadas diferentes. Únicos porque são dependentes de dados, e esses dados variam muito de um caso de uso para outro. Neste livro, você aprenderá uma abordagem holística para projetar sistemas de ML que sej...
2024
Técnicas de machine learning
O desenvolvimento de modelos de análise aplicados à realidade empresarial se tornou competência crítica em todos os setores de atividade econômica e os profissionais enfrentam duas questões. Como geraremos valor a partir dos dados? Como usaremos corretamente os métodos de análise? Existe um crescente arsenal de técnicas de Machine Learning, provocando efeitos colaterais indesejáveis, como a adoção...
2024
Python para Data Science
A era da informação trouxe consigo uma avalanche nunca vista na criação de entendimento digital. O acesso e a interpretação desses dados tornaram-se um diferencial estratégico para empresas e também para governos, e podem dar suporte a estudos em várias áreas, como Economia e Política, passando pelas Ciências Sociais. É nesse contexto que as ferramentas necessárias para o tratamento desses dados ...
Big data and machine learning in quantitative investment
Big data and machine learning in quantitative investment
2019
Livro iot streams for data-driven predictive maintenance and iot, edge, and mobile for embedded machine learning
Livro iot streams for data-driven predictive maintenance and iot, edge, and mobile for embedded machine learning
2021
Livro machine learning for kids (tinker toddlers)
Livro machine learning for kids (tinker toddlers)
2019
Machine learning methods in the environmental sciences
Machine learning methods in the environmental sciences
2009
Machine Learning for Hackers
<DIV><P>If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional m...
2012
Probability for statistics and machine learning
Probability for statistics and machine learning
2011
Statistical and machine learning approaches for network ana
Statistical and machine learning approaches for network ana
2012
Building Machine Learning Systems with Python - Third Edition
<p><strong style="color: rgba(68, 68, 68, 1)">Build intelligent end-to-end machine learning systems with Python</strong></p><p><strong>Key Features</strong></p><ul><li>Use scikit-learn and TensorFlow to train your machine learning models</li><li>Implement popular supervised and unsupervised machine learning algorithms in Python</li><li>Discover best practices for building production-grade machine ...
2018
Graph Machine Learning
<p><strong>Build machine learning algorithms using graph data and efficiently exploit topological information within your models</strong></p><p><br></p><p><strong>Key Features:</strong></p><ul><li>Implement machine learning techniques and algorithms in graph data</li><li>Identify the relationship between nodes in order to make better business decisions</li><li>Apply graph-based machine learning me...
2021
Hands-On Machine Learning with C++
<p><strong>Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets</strong></p><p><strong>Key Features</strong></p> <ul> <li>Become familiar with data processing, performance measuring, and model selection using various C++ libraries</li> <li>Impleme...
2020
Interpretable Machine Learning with Python
<p><strong>Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models</strong></p><p><br></p><p><strong>Key Features:</strong></p><ul><li>Learn how to extract easy-to-understand insights from any machine learning model</li><li>Become well-versed with inter...
2021
Machine Learning Engineering with Python
<p><strong>Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments</strong></p><p><br></p><p><strong>Key Features:</strong></p><ul><li>Explore hyperparameter optimization and model management tools</li><li>Learn object-oriented programming and functional programming in Python to build your own ML libraries and p...
2021
Machine Learning Algorithms - Second Edition
<p><strong>An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms</strong></p> <p><strong>Key Features</strong></p> <ul><li>Explore statistics and complex mathematics for data-intensive applications </li> <li>Discover new developments in EM algorithm, PCA, and bayesian regression </li> <li>Study patterns and make predictions across...
2018
Machine learning concepts with python and the jupyter noteb
Machine learning concepts with python and the jupyter noteb
2020
Mastering Machine Learning Algorithms - Second Edition
Mastering Machine Learning Algorithms - Second Edition
2020
Pattern Recognition and Machine Learning
<P>This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition ...
2016
Python Machine Learning
<p><strong>Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.</strong></p><p><strong>Key Features</strong></p> <ul> <li>Third edition of the bestselling, widely acclaimed Python machine learning book</li> <li>Clear and intuitive explanations take you deep into the theory and practice of Python machine learning</li>...
2019
Statistics for Machine Learning
<p><strong>Build Machine Learning models with a sound statistical understanding.</strong></p><p><br></p><p><strong>Key Features:</strong></p><ul><li>Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.</li><li>Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.</li><li>Master the st...
2017
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
<p><strong>Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems</strong></p><p><strong>Key Features</strong></p> <ul> <li>Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python</li> <li>Master the art of data-driven problem-solving with hands-on exam...
2020
Machine Learning with PySpark
<div><p>Chapter 1: Introduction to Spark 3.1</p><p>Chapter Goal: The book's opening chapter introduces the readers to latest changes in PySpark and updates to the framework. This chapter covers the different components of Spark ecosystem. The chapter doubles up as an introduction to the book's format, including explanation of formatting practices, pointers to the book's accompanying codebase...
2021
MATLAB for Machine Learning
<p><strong>Extract patterns and knowledge from your data in easy way using MATLAB</strong></p><p> </p><p><strong>Key Features</strong></p><ul><li>Get your first steps into machine learning with the help of this easy-to-follow guide</li><li>Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB</li><li>Understand how your data works and i...
2017