Introduction to Machine Learning with Python: A Guide for Data Scientists - Marjane Mall - Image 1
245
00DH
490.00 DH
-50%

Introduction to Machine Learning with Python: A Guide for Data Scientists

Livraison

Détails
Frais de livraison à partir de :
Livraison entre le Mercredi 9 septembre 2026 et le Jeudi 10 septembre 2026

À propos de cet article :

Marque : GENERIC
Vendu par HEAVENBOOKS.MA

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine ...

1

Mode de paiement

Paiement par carte bancaire
Carte marocaines
Paiement à la livraison
Paiement en espèce à la livraison
Politique de retours
Note de politique de retour

Description produit

Marque
GENERIC
Titre
Introduction to Machine Learning with Python: A Guide for Data Scientists
Éditeur
O'Reilly Media
Type de produit
paperback
Présentation du livre
paperback
Date de sortie
11/15/2016 12:00:00 AM
Langue d'origine
English
ISBN
1449369413
Nombre de pages
400 pages
Langue
English
Résumé
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you’ll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills Read more
Auteur
Andreas C. Müller, Sarah Guido
Date de parution
11/15/2016 12:00:00 AM