Python Machine Learning 2021 2022
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Python Machine Learning 2021 2022
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Author : Victor London
language : en
Publisher: Victor London
Release Date : 2021-06-08
Python Machine Learning 2021 2022 written by Victor London and has been published by Victor London this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-08 with categories.
★ 55% OFF for Bookstores! ★ If you are looking for a comprehensive guide that explains in a simple way how to manage machine learning and AI, please keep reading. What if you could make your own program, one that is able to learn by trial and error, or based on the information that you show it? What if you could get a program that could adapt and change based on the input of the user? And what if you were able to make all of this happen with the Python coding language, helping even beginner's work with more complicated codes? This is all possible with Python machine learning. This guidebook is going to take some time to look at Python machine learning and all of the neat things that you are able to do with it. Machine learning is a growing field, one that a lot of programmers want to spend their time on. But even though this sounds like a complicated part of technology to work with, you will find that with the help of the Python coding language, anyone can start writing their own codes in machine learning. This guidebook is going to take a look at all of the different topics that you need to know in order to get started with Python machine learning. Some of the topics that we will explore inside include: The basics of machine learning The difference between supervised and unsupervised machine learning. Setting up your new environment in the Python language. Data preprocessing with the help of machine learning. How to use Python coding to help with linear regression. Decision trees and random forests. How to work with support vector regression problems. Can machine learning really help with Naïve Bayes problems? Accelerated data analysis using the Python code. And so much more! If you have been interested in learning more about machine learning, and you want to be able to learn a few of the codes that can make it happen for you, make sure to check out this guidebook to help you get started!
Machine Learning For Civil And Environmental Engineers
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Author : M. Z. Naser
language : en
Publisher: John Wiley & Sons
Release Date : 2023-08-08
Machine Learning For Civil And Environmental Engineers written by M. Z. Naser and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-08 with Technology & Engineering categories.
Accessible and practical framework for machine learning applications and solutions for civil and environmental engineers This textbook introduces engineers and engineering students to the applications of artificial intelligence (AI), machine learning (ML), and machine intelligence (MI) in relation to civil and environmental engineering projects and problems, presenting state-of-the-art methodologies and techniques to develop and implement algorithms in the engineering domain. Through real-world projects like analysis and design of structural members, optimizing concrete mixtures for site applications, examining concrete cracking via computer vision, evaluating the response of bridges to hazards, and predicating water quality and energy expenditure in buildings, this textbook offers readers in-depth case studies with solved problems that are commonly faced by civil and environmental engineers. The approaches presented range from simplified to advanced methods, incorporating coding-based and coding-free techniques. Professional engineers and engineering students will find value in the step-by-step examples that are accompanied by sample databases and codes for readers to practice with. Written by a highly qualified professional with significant experience in the field, Machine Learning includes valuable information on: The current state of machine learning and causality in civil and environmental engineering as viewed through a scientometrics analysis, plus a historical perspective Supervised vs. unsupervised learning for regression, classification, and clustering problems Explainable and causal methods for practical engineering problems Database development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysis A framework for machine learning adoption and application, covering key questions commonly faced by practitioners This textbook is a must-have reference for undergraduate/graduate students to learn concepts on the use of machine learning, for scientists/researchers to learn how to integrate machine learning into civil and environmental engineering, and for design/engineering professionals as a reference guide for undertaking MI design, simulation, and optimization for infrastructure.
Applied Machine Learning For Data Science Practitioners
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Author : Vidya Subramanian
language : en
Publisher: John Wiley & Sons
Release Date : 2025-05-28
Applied Machine Learning For Data Science Practitioners written by Vidya Subramanian and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-28 with Mathematics categories.
Single volume reference on using various aspects of data science to evaluate, understand, and solve business problems A reference book for anyone in the field of data science, Applied Machine Learning for Data Science Practitioners walks readers through the end-to-end process of solving any machine learning problem by identifying, choosing, and applying the right solution for the issue at hand. The text enables readers to figure out optimal validation techniques based on the use case and data orientation, choose a range of pertinent models from different types of learning, and score models to apply metrics across all the estimators evaluated. Unlike most books on data science in today's market that jump right into algorithms and coding and focus on the most-used algorithms, this text helps data scientists evaluate all pertinent techniques and algorithms to assess all these machine learning problems and suitable solutions. Readers can make an informed decision on which models and validation techniques to use based on the business problem, data availability, desired outcome, and more. Written by an internationally recognized author in the field of data science, Applied Machine Learning for Data Science Practitioners also covers topics such as: Data preparation, including basic data cleaning, integration, transformation, and compression methods, along with data visualization and exploratory analyses Cross-validation in model validation techniques, including independent, identically distributed, imbalanced, blocked, and grouped data Prediction using regression models and classification using classification models, with applicable performance measurements for each Types of clustering in clustering models based on partition, hierarchy, fuzzy theory, distribution, density, and graph theory Detecting anomalies, including types of anomalies and key terms like noise, rare events, and outliers Applied Machine Learning for Data Science Practitioners is an essential resource for all data scientists and business professionals to cross-validate a range of different algorithms to find an optimal solution. Readers are assumed to have a basic understanding of solving business problems using data, high school level math, statistics, and coding skills.
Machine Learning In Modeling And Simulation
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Author : Timon Rabczuk
language : en
Publisher: Springer Nature
Release Date : 2023-10-03
Machine Learning In Modeling And Simulation written by Timon Rabczuk and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-03 with Technology & Engineering categories.
Machine learning (ML) approaches have been extensively and successfully employed in various areas, like in economics, medical predictions, face recognition, credit card fraud detection, and spam filtering. There is clearly also the potential that ML techniques developed in Engineering and the Sciences will drastically increase the possibilities of analysis and accelerate the design to analysis time. With the use of ML techniques, coupled to conventional methods like finite element and digital twin technologies, new avenues of modeling and simulation can be opened but the potential of these ML techniques needs to still be fully harvested, with the methods developed and enhanced. The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling in Engineering and the Sciences in which the exciting aspects of machine learning are incorporated. The book is of value to any researcher and practitioner interested in research or applications of ML in the areas of scientific modeling and computer aided engineering.
Machine Learning And Knowledge Discovery In Databases Applied Data Science And Demo Track
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Author : Gianmarco De Francisci Morales
language : en
Publisher: Springer Nature
Release Date : 2023-09-16
Machine Learning And Knowledge Discovery In Databases Applied Data Science And Demo Track written by Gianmarco De Francisci Morales and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-16 with Computers categories.
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: Robustness; Time Series; Transfer and Multitask Learning. Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Machine Learning For Biological Sequence Analysis
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Author : Quan Zou
language : en
Publisher: Frontiers Media SA
Release Date : 2023-03-09
Machine Learning For Biological Sequence Analysis written by Quan Zou and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-09 with Science categories.
The Pragmatic Programmer For Machine Learning
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Author : Marco Scutari
language : en
Publisher: CRC Press
Release Date : 2023-03-31
The Pragmatic Programmer For Machine Learning written by Marco Scutari and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-31 with Computers categories.
Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions discusses how modern software engineering practices are part of this revolution both conceptually and in practical applictions. Comprising a broad overview of how to design machine learning pipelines as well as the state-of-the-art tools we use to make them, this book provides a multi-disciplinary view of how traditional software engineering can be adapted to and integrated with the workflows of domain experts and probabilistic models. From choosing the right hardware to designing effective pipelines architectures and adopting software development best practices, this guide will appeal to machine learning and data science specialists, whilst also laying out key high-level principlesin a way that is approachable for students of computer science and aspiring programmers.
Machine Learning For Real World Applications
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Author : Dinesh K. Sharma
language : en
Publisher: Springer Nature
Release Date : 2024-09-20
Machine Learning For Real World Applications written by Dinesh K. Sharma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-20 with Mathematics categories.
This book provides a comprehensive coverage of machine learning techniques ranging from fundamental to advanced. The content addresses topics within the scope of the book from the ground up, providing readers with a trustworthy source of theoretical and technical learning content. The book emphasizes not only the theoretical features but also their practical and implementation aspects in real-world applications. These applications are crucial because they provide comprehensive experimental work that supports the validity of the offered approaches as well as clear instructions on how to apply such models in comparable and distinct settings and contexts. Furthermore, the chapters shed light on the problems and possibilities that researchers might use to direct their future research efforts. The book is beneficial for undergraduate and postgraduate students, researchers, and industry personnel.
The 22nd International Conference On Information Technology New Generations Itng 2025
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Author : Shahram Latifi
language : en
Publisher: Springer Nature
Release Date : 2025-05-08
The 22nd International Conference On Information Technology New Generations Itng 2025 written by Shahram Latifi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-08 with Computers categories.
This book covers technical contributions that have been submitted, reviewed and presented at the 22nd annual event of International conference on Information Technology: New Generations (ITNG) The applications of advanced information technology to such domains as astronomy, biology, education, geosciences, security and health care are among topics of relevance to ITNG. Visionary ideas, theoretical and experimental results, as well as prototypes, designs, and tools that help the information readily flow to the user are of special interest. Machine Learning, Robotics, High Performance Computing, and Innovative Methods of Computing are examples of related topics.
Machine Learning Deep Learning And Ai For Cybersecurity
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Author : Mark Stamp
language : en
Publisher: Springer Nature
Release Date : 2025-05-09
Machine Learning Deep Learning And Ai For Cybersecurity written by Mark Stamp and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-09 with Mathematics categories.
This book addresses a variety of problems that arise at the interface between AI techniques and challenging problems in cybersecurity. The book covers many of the issues that arise when applying AI and deep learning algorithms to inherently difficult problems in the security domain, such as malware detection and analysis, intrusion detection, spam detection, and various other subfields of cybersecurity. The book places particular attention on data driven approaches, where minimal expert domain knowledge is required. This book bridges some of the gaps that exist between deep learning/AI research and practical problems in cybersecurity. The proposed topics cover a wide range of deep learning and AI techniques, including novel frameworks and development tools enabling the audience to innovate with these cutting-edge research advancements in various security-related use cases. The book is timely since it is not common to find clearly elucidated research that applies the latest developments in AI to problems in cybersecurity.