Data cleaning and exploration with machine learning : get to grips with machine learning techniques to achieve sparkling-clean data quickly /

Explore supercharged machine learning techniques to take care of your data laundry loads Key Features Learn how to prepare data for machine learning processes Understand which algorithms are based on prediction objectives and the properties of the data Explore how to interpret and evaluate the resul...

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Bibliographic Details
Call Number:Libro Electrónico
Main Author: Walker, Michael
Format: Electronic eBook
Language:Inglés
Published: Birmingham : Packt Publishing, Limited, 2022.
Subjects:
Data mining.
Machine learning.
Exploration de données (Informatique)
Apprentissage automatique.
Data mining
Machine learning
Online Access:Texto completo (Requiere registro previo con correo institucional)
Table of Contents:
  • Section 1: Data Cleaning and Machine Learning Algorithms
  • Examining the Distribution of Features and Targets
  • Examining Bivariate and Multivariate Relationships between Features and Targets
  • Identifying and Fixing Missing Values
  • Section 2: Preprocessing, Feature Selection, and Sampling
  • Encoding, Transforming, and Scaling Features
  • Feature Selection
  • Preparing for Model Evaluation
  • Section 3: Modeling Continuous Targets with Supervised Learning
  • Linear Regression Models
  • Support Vector Regression
  • K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosted Regression
  • Section 4: Modeling Dichotomous and Multiclass Targets with Supervised Learning
  • Logistic Regression
  • Decision Trees and Random Forest Classification
  • K-Nearest Neighbors for Classification
  • Support Vector Machine Classification
  • Naïve Bayes Classification
  • Section 5: Clustering and Dimensionality Reduction with Unsupervised Learning
  • Principal Component Analysis
  • K-Means and DBSCAN Clustering.

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