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portada Machine Learning: From Concepts to Implementations: A Comprehensive Guide
Type
Physical Book
Publisher
Language
Inglés
Pages
130
Format
Paperback
Dimensions
22.9 x 15.2 x 0.7 cm
Weight
0.18 kg.
ISBN13
9789635222902

Machine Learning: From Concepts to Implementations: A Comprehensive Guide

Christopher Harrison (Author) · Publishdrive · Paperback

Machine Learning: From Concepts to Implementations: A Comprehensive Guide - Harrison, Christopher

New Book

£ 18.73

  • Condition: New
Origin: U.S.A. (Import costs included in the price)
It will be shipped from our warehouse between Friday, August 09 and Wednesday, August 21.
You will receive it anywhere in United Kingdom between 1 and 3 business days after shipment.

Synopsis "Machine Learning: From Concepts to Implementations: A Comprehensive Guide"

In an age where data reigns supreme, harnessing this data's power and making intelligent decisions has become a quintessential skill for businesses, researchers, and even hobbyists. Machine Learning - a fascinating realm where machines gain the ability to learn from experience, adapt, and, most impressively, predict future outcomes. This transformative technology powers many of the applications we use daily, from recommendation systems on our favorite streaming platforms to the intelligent assistants on our smartphones. "Mastering Machine Learning: From Concepts to Implementations - A Comprehensive Guide" is designed to be your navigator in this expansive universe. Whether you're a novice intrigued by the buzzwords or a professional aiming to sharpen your implementational skills, this guide offers a structured journey through the foundational concepts, the intricate algorithms, and, most importantly, the practical implementations. Beyond the theoretical constructs and coding tutorials, this e-book delves into the broader implications of machine learning. As we'll see, with great power comes great responsibility. The ethical considerations, the realworld impacts, and the future trajectories of this field are as vital as understanding the difference between supervised and unsupervised learning.

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