Machine Learning For Science And Engineering Volume 1 Fundamentals
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Machine Learning For Science And Engineering Volume 1 Fundamentals
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Author : Herman Jaramillo
language : en
Publisher: SEG Books
Release Date : 2023-04-01
Machine Learning For Science And Engineering Volume 1 Fundamentals written by Herman Jaramillo and has been published by SEG Books this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-01 with Science categories.
This textbook teaches underlying mathematics, terminology, and programmatic skills to implement, test, and apply machine learning to real-world problems. Exercises with field data, including well logs and weather measurements, prepare and encourage readers to begin using software to validate results and program their own creative data solutions. As the size and complexity of data soars exponentially, machine learning (ML) has gained prominence in applications in geoscience and related fields. ML-powered technology increasingly rivals or surpasses human performance and fuels a large range of leading-edge research. This textbook teaches the underlying mathematics, terminology, and programmatic skills to implement, test, and apply ML to real-world problems. It builds the mathematical pillars required to thoroughly comprehend and master modern ML concepts and translates the newly gained mathematical understanding into better applied data science. Exercises with raw field data, including well logs and weather measurements, prepare and encourage the reader to begin using software to validate results and program their own creative data solutions. Most importantly, the reader always keeps an eye on the ML’s imperfect data situations as encountered in the real world.
Applied Machine Learning For Data Science Practitioners
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Author : Vidya Subramanian
language : en
Publisher: John Wiley & Sons
Release Date : 2025-04-01
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 2025-04-01 with Mathematics categories.
A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML. Data Preparation covers the process of framing ML problems and preparing data and features for modeling. ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection. Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model. ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics. Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift.
Deep Learning And Its Applications Using Python
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Author : Niha Kamal Basha
language : en
Publisher: John Wiley & Sons
Release Date : 2023-10-31
Deep Learning And Its Applications Using Python written by Niha Kamal Basha 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 2023-10-31 with Computers categories.
This book thoroughly explains deep learning models and how to use Python programming to implement them in applications such as NLP, face detection, face recognition, face analysis, and virtual assistance (chatbot, machine translation, etc.). It provides hands-on guidance in using Python for implementing deep learning application models. It also identifies future research directions for deep learning.
A Greater Foundation For Machine Learning Engineering
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Author : Dr. Ganapathi Pulipaka
language : en
Publisher: Xlibris Corporation
Release Date : 2021-10-01
A Greater Foundation For Machine Learning Engineering written by Dr. Ganapathi Pulipaka and has been published by Xlibris Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-01 with Computers categories.
This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning. The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods. The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms. This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.
A Primer On Machine Learning In Subsurface Geosciences
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Author : Shuvajit Bhattacharya
language : en
Publisher: Springer Nature
Release Date : 2021-05-03
A Primer On Machine Learning In Subsurface Geosciences written by Shuvajit Bhattacharya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-03 with Technology & Engineering categories.
This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences.
Probabilistic Reasoning And Decision Making In Sensory Motor Systems
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Author : Pierre Bessière
language : en
Publisher: Springer
Release Date : 2008-08-27
Probabilistic Reasoning And Decision Making In Sensory Motor Systems written by Pierre Bessière and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-27 with Technology & Engineering categories.
Probabilistic Reasoning and Decision Making in Sensory-Motor Systems by Pierre Bessiere, Christian Laugier and Roland Siegwart provides a unique collection of a sizable segment of the cognitive systems research community in Europe. It reports on contributions from leading academic institutions brought together within the European projects Bayesian Inspired Brain and Artifact (BIBA) and Bayesian Approach to Cognitive Systems (BACS). This fourteen-chapter volume covers important research along two main lines: new probabilistic models and algorithms for perception and action, new probabilistic methodology and techniques for artefact conception and development. The work addresses key issues concerned with Bayesian programming, navigation, filtering, modelling and mapping, with applications in a number of different contexts.
Fundamentals Of Machine Learning For Predictive Data Analytics Second Edition
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Author : John D. Kelleher
language : en
Publisher: MIT Press
Release Date : 2020-10-20
Fundamentals Of Machine Learning For Predictive Data Analytics Second Edition written by John D. Kelleher and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-20 with Computers categories.
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning. The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.
System Sustainment Acquisition And Engineering Processes For The Sustainment Of Critical And Legacy Systems
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Author : Peter Sandborn
language : en
Publisher: World Scientific
Release Date : 2022-08-16
System Sustainment Acquisition And Engineering Processes For The Sustainment Of Critical And Legacy Systems written by Peter Sandborn and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-16 with Technology & Engineering categories.
'Sustainment' (as commonly defined by industry and government), is comprised of maintenance, support, and upgrade practices that sustain or improve the performance of a system and maximize the availability of goods and services while minimizing their cost and footprint or, more simply, the capacity of a system to endure. Sustainment is a multi-trillion-dollar enterprise for critical systems, in both government (infrastructure and defense) and industry (transportation, industrial controls, data centers, and energy generation).This book is a mix of engineering, operations research, and policy sciences intended to provide students with a thorough understanding of the concept of sustainability and sustainable product life-cycles, and an appreciation of the importance of sustaining critical systems. It starts from the key attributes for system sustainment that includes data analytics, engineering analysis and the public policy needed to support the development of technologies, processes, and frameworks required for the management of sustainable processes and practices. The specific topics covered include: acquisition of critical systems, reliability, maintenance, availability, readiness, inventory management, supply-chain management and risks, contracting for sustainment, and various analysis methodologies (discounted cash flow analysis, discrete-event simulation and Monte Carlo methods). Practice problems are included at the end of each chapter.
Industrial Ventilation Design Guidebook
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Author : Howard D. Goodfellow
language : en
Publisher: Academic Press
Release Date : 2021-06-04
Industrial Ventilation Design Guidebook written by Howard D. Goodfellow and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-04 with Technology & Engineering categories.
Industrial Ventilation Design Guidebook, Volume 2: Engineering Design and Applications brings together researchers, engineers (both design and plants), and scientists to develop a fundamental scientific understanding of ventilation to help engineers implement state-of-the-art ventilation and contaminant control technology. Now in two volumes, this reference contains extensive revisions and updates as well as a unique section on best practices for the following industrial sectors: Automotive; Cement; Biomass Gasifiers; Advanced Manufacturing; Industrial 4.0); Non-ferrous Smelters; Lime Kilns; Pulp and Paper; Semiconductor Industry; Steelmaking; Mining. - Brings together global researchers and engineers to solve complex ventilation and contaminant control problems using state-of-the-art design equations - Includes an expanded section on modeling and its practical applications based on recent advances in research - Features a new chapter on best practices for specific industrial sectors
Engineering Analytics
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Author : Luis Rabelo
language : en
Publisher: CRC Press
Release Date : 2021-09-26
Engineering Analytics written by Luis Rabelo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-26 with Business & Economics categories.
Engineering analytics is becoming a necessary skill for every engineer. Areas such as Operations Research, Simulation, and Machine Learning can be totally transformed through massive volumes of data. This book is intended to be an introduction to Engineering Analytics that can be used to improve performance tracking, customer segmentation for resource optimization, patterns and classification strategies, and logistics control towers. Basic methods in the areas of visual, descriptive, predictive, and prescriptive analytics and Big Data are introduced. Industrial case studies and example problem demonstrations are used throughout the book to reinforce the concepts and applications. The book goes on to cover visual analytics and its relationships, simulation from the respective dimensions and Machine Learning and Artificial Intelligence from different paradigms viewpoints. The book is intended for professionals wanting to work on analytical problems, for Engineering students, Researchers, Chief-Technology Officers, and Directors that work within the areas and fields of Industrial Engineering, Computer Science, Statistics, Electrical Engineering Operations Research, and Big Data.