Search

Categories

    • categories-img Jacket, Women
    • categories-img Woolend Jacket
    • categories-img Western denim
    • categories-img Mini Dresss
    • categories-img Jacket, Women
    • categories-img Woolend Jacket
    • categories-img Western denim
    • categories-img Mini Dresss
    • categories-img Jacket, Women
    • categories-img Woolend Jacket
    • categories-img Western denim
    • categories-img Mini Dresss
    • categories-img Jacket, Women
    • categories-img Woolend Jacket
    • categories-img Western denim
    • categories-img Mini Dresss
    • categories-img Jacket, Women
    • categories-img Woolend Jacket
    • categories-img Western denim
    • categories-img Mini Dresss

Filter By Price

$
-
$

Dietary Needs

Top Rated Product

product-img product-img

Modern Chair

$165.00
product-img product-img

Plastic Chair

$165.00
product-img product-img

Design Rooms

$165.00

Brands

  • Wooden
  • Chair
  • Modern
  • Fabric
  • Shoulder
  • Winter
  • Accessories
  • Dress

Welcome and thank you for visiting us. For any query call us on 0799 626 359 or Email [email protected]

Shopping Cart

Africa largest book store

Sub Total:

Search for any Title

Fundamentals of Robust Machine Learning : Handling Outliers and Anomalies in Data Science

By: (Author) A. K. Md. Ehsanes Saleh , (Author) Resve A. Saleh , (Author) Sohaib Majzoub

Extended Catalogue

Ksh 15,550.00

Format: Hardback or Cased Book

ISBN-10: 1394294379

ISBN-13: 9781394294374

Publisher: John Wiley & Sons Inc

Imprint: John Wiley & Sons Inc

Country of Manufacture: US

Country of Publication: GB

Publication Date: May 9th, 2025

Print length: 416 Pages

Weight: 839 grams

Dimensions (height x width x thickness): 23.40 x 18.80 x 2.80 cms

Product Classification: Data capture & analysis
Data mining
Machine learning

Choose your Location

Shipping & Delivery

Door Delivery

Delivery fee

Delivery in 10 to 14 days

  • Description

  • Reviews

An essential guide for tackling outliers and anomalies in machine learning and data science. In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few. Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models. Fundamentals of Robust Machine Learning readers will also find: A blend of robust statistics and machine learning principlesDetailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detectionPython code with immediate application to data science problems Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.

An essential guide for tackling outliers and anomalies in machine learning and data science.

In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few.

Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on the how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using standard tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models.

Fundamentals of Robust Machine Learning readers will also find:

  • A blend of robust statistics and machine learning principles
  • Detailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detection
  • Python code with immediate application to data science problems

Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.

Get Fundamentals of Robust Machine Learning by at the best price and quality guranteed only at Werezi Africa largest book ecommerce store. The book was published by John Wiley & Sons Inc and it has pages. Enjoy Shopping Best Offers & Deals on books Online from Werezi - Receive at your doorstep - Fast Delivery - Secure mode of Payment

Customer Reviews

Based on 0 reviews

Mind, Body, & Spirit