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Start your reading journey today on DL4ALL.org and unlock a world of imagination, knowledge, and inspiration!  Free Download Machine Learning with R: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data, 4th Edition by Brett Lantz English | May 29th, 2023 | ISBN: 1801071322 | 762 pages | True EPUB (Retail EPUB) | 27.56 MB Use R and tidyverse to prepare, clean, import, visualize, transform, program, communicate, predict and model data No R experience is required, although prior exposure to statistics and programming is helpful  Free Download Machine Learning for Materials Discovery: Numerical Recipes and Practical Applications (Machine Intelligence for Materials Science) by N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo English | May 7, 2024 | ISBN: 3031446216 | 299 pages | MOBI | 39 Mb Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect―each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials.  Free Download Machine Learning for Healthcare by Rasit Dinc English | July 21, 2024 | ISBN: 1805149415 | 536 pages | EPUB | 0.81 Mb  Free Download Machine Learning for Emotion Analysis in Python: Build AI-powered tools for analyzing emotion using natural language processing and machine learning by Allan Ramsay, Tariq Ahmad English | September 28, 2023 | ISBN: 1803240687 | 334 pages | PDF | 16 Mb Kickstart your emotion analysis journey with this step-by-step guide to data science success  Free Download Machine Learning for Beginners by Aldrich Hill English | August 9, 2024 | ISBN: 821550008X | 109 pages | EPUB | 1.15 Mb  Free Download Machine Learning Techniques and Industry Applications by Pramod Kumar Srivastava, Ashok Kumar Yadav English | April 16, 2024 | ISBN: 8369352723 | 332 pages | EPUB | 16 Mb In today's rapidly evolving world, the exponential growth of data poses a significant challenge. As data volumes increase, traditional methods of analysis and decision-making become inadequate. This surge in data complexity calls for innovative solutions that efficiently extract meaningful insights. Machine learning has emerged as a powerful tool to address this challenge, offering algorithms and techniques to analyze large datasets and uncover hidden patterns, trends, and correlations. Machine Learning Techniques and Industry Applications demystifies machine learning through detailed explanations, examples, and case studies, making it accessible to a broad audience. Whether you're a student, researcher, or practitioner, this book equips you with the knowledge and skills needed to harness the power of machine learning to address diverse challenges. From e-government to healthcare, cyber-physical systems to agriculture, this book explores how machine learning can drive innovation and sustainable development.  Free Download Machine Learning Methods for Multi-Omics Data Integration by Abedalrhman Alkhateeb, Luis Rueda English | November 14, 2023 | ISBN: 3031365011 | 174 pages | MOBI | 7.74 Mb The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integratingthese large-scale heterogeneous data sets into one learning model.  Free Download Machine Learning Mathematics by Jackson Smart English | August 9, 2024 | ISBN: 8227834663 | 76 pages | EPUB | 0.87 Mb  Free Download Machine Learning Infrastructure and Best Practices for Software Engineers: Take your machine learning software from a prototype to a fully fledged software system by Miroslaw Staron English | January 31, 2024 | ISBN: 1837634068 | 346 pages | EPUB | 13 Mb Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software productsKey FeaturesLearn how to scale-up your machine learning software to a professional levelSecure the quality of your machine learning pipeline at runtimeApply your knowledge to natural languages, programming languages, and imagesBook Description  Free Download Hui Jiang, "Machine Learning Fundamentals" English | ISBN: 1108940021 | 2022 | 418 pages | AZW3 | 6 MB This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely "from scratch" based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts. |
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