Machine Learning 101 with Scikit-learn and StatsModels

New to machine learning? This is the place to start: Linear regression, Logistic regression, and Cluster Analysis About This Video Learn machine learning with StatsModels and sklearn Apply machine learning skills to solve real-world business cases Get started with linear regression, logistic regress...

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Detalles Bibliográficos
Otros Autores: Careers, 365, author (author)
Formato: Video
Idioma:Inglés
Publicado: Packt Publishing 2019.
Edición:1st edition
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631356506719
Descripción
Sumario:New to machine learning? This is the place to start: Linear regression, Logistic regression, and Cluster Analysis About This Video Learn machine learning with StatsModels and sklearn Apply machine learning skills to solve real-world business cases Get started with linear regression, logistic regression, and cluster analysis In Detail Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist!
Notas:Title from resource description page (Safari, viewed February 18, 2020).
Descripción Física:1 online resource (1 video file, approximately 5 hr., 13 min.)