Name: Mastering Machine Learning for Data Scientists (en Inglés)
SKU: 59803125
Price: 106.33 USD
Availability: InStock
Author: Rama Rao, N. ; Mahamkali, Aditya
ISBN: 9786207476015

portada Mastering Machine Learning for Data Scientists (en Inglés)

Mastering Machine Learning for Data Scientists (en Inglés)

Formato

Libro Físico

Autor

N. Rama Rao

Aditya Mahamkali

Editorial

LAP Lambert Academic Publishing

Idioma

Inglés

N° páginas

184

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x1.1 cm

Peso

0.28 kg.

ISBN13

9786207476015

Categorías

Tecnología: cuestiones generales

Mastering Machine Learning for Data Scientists (en Inglés)

N. Rama Rao (Autor) · Aditya Mahamkali (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

Libro Nuevo Importado

Envío: 26 a 33 días háb.

$ 177.22$ 106.33

-40%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 10 unidades

$ 106.33

Llega entre el 06 Nov y el 19 Nov a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Mastering Machine Learning for Data Scientists (en Inglés)"

In this book we embark on a fascinating journey through various techniques and algorithms crucial for modern data scientists. As the field of data science rapidly evolves, machine learning stands as a cornerstone technology, facilitating organizations in extracting valuable insights from vast datasets to drive informed decision-making. Whether you're a seasoned data scientist seeking to deepen your expertise or a newcomer eager to explore the world of machine learning, this book is tailored to meet your needs. Structured around twelve comprehensive chapters, each focusing on a specific aspect of machine learning. From Supervised Machine Learning to Unsupervised Machine Learning, Ensemble Machine Learning, and Self-Supervised Learning, each chapter delves into essential concepts and practical applications, providing clear explanations and hands-on examples to aid comprehension and skill development. Whether you're looking to understand the fundamentals of supervised learning, uncover hidden patterns with unsupervised techniques, harness the power of ensemble methods, or explore cutting-edge self-supervised learning approaches.