Data Cleaning And Exploration With Machine Learning
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Data Cleaning and Exploration with Machine Learning
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 results from machine learning Book Description: Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results. As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You'll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you'll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You'll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book. By the end of this book, you'll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering. What You Will Learn: Explore essential data cleaning and exploration techniques to be used before running the most popular machine learning algorithms Understand how to perform preprocessing and feature selection, and how to set up the data for testing and validation Model continuous targets with supervised learning algorithms Model binary and multiclass targets with supervised learning algorithms Execute clustering and dimension reduction with unsupervised learning algorithms Understand how to use regression trees to model a continuous target Who this book is for: This book is for professional data scientists, particularly those in the first few years of their career, or more experienced analysts who are relatively new to machine learning. Readers should have prior knowledge of concepts in statistics typically taught in an undergraduate introductory course as well as beginner-level experience in manipulating data programmatically.
Hands-On Simulation Modeling with Python
Author: Giuseppe Ciaburro
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-11-30
Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with ease Key FeaturesUnderstand various statistical and physical simulations to improve systems using PythonLearn to create the numerical prototype of a real model using hands-on examplesEvaluate performance and output results based on how the prototype would work in the real worldBook Description Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that'll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you'll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you'll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges. What you will learnGet to grips with the concept of randomness and the data generation processDelve into resampling methodsDiscover how to work with Monte Carlo simulationsUtilize simulations to improve or optimize systemsFind out how to run efficient simulations to analyze real-world systemsUnderstand how to simulate random walks using Markov chainsWho this book is for This book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.
Artificial Intelligence and Machine Learning
The two-volume proceedings set CCIS 2299 and 2300, constitutes the refereed proceedings of the 43rd IBIMA Conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024, held in Madrid, Spain, in June 26–27, 2024. The 44 full papers and 18 short papers included in this book were carefully reviewed and selected from 119 submissions. They were organized in topical sections as follows: Part I:Artificial Intelligence and Machine Learning; Information Systems and Communications Technologies. Part II: Artificial Intelligence and Machine Learning ; Software Engineering; Computer Security and Privacy.