Machine Learning Essentials Algorithms And Applications
DOWNLOAD
Download Machine Learning Essentials Algorithms And Applications PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Machine Learning Essentials Algorithms And Applications book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Machine Learning Essentials Algorithms And Applications
DOWNLOAD
Author : Mrs.Shambhavi
language : en
Publisher: SK Research Group of Companies
Release Date : 2025-04-10
Machine Learning Essentials Algorithms And Applications written by Mrs.Shambhavi and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-10 with Computers categories.
Mrs.Shambhavi, Assistant Professor, Department of Computer Science and Engineering, Don Bosco Institute of Technology, Bangalore, Karnataka, India. Ms.Sinchana K.P, Assistant Professor, Department of Computer Science and Engineering, Don Bosco Institute of Technology, Bangalore, Karnataka, India. Mrs.Thejaswini D, Assistant Professor, Department of Computer Science and Engineering, Don Bosco Institute of Technology, Bangalore, Karnataka, India. Mrs.Geethanjali S.G, Assistant Professor, Department of Computer Science and Engineering, Don Bosco Institute of Technology, Bangalore, Karnataka, India.
Machine Learning Essentials You Always Wanted To Know
DOWNLOAD
Author : Dhairya Parikh
language : en
Publisher: Vibrant Publishers
Release Date : 2025-07-04
Machine Learning Essentials You Always Wanted To Know written by Dhairya Parikh and has been published by Vibrant Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-04 with Business & Economics categories.
· Covers key algorithms and techniques · Ideal for students and professionals · Hands-on implementation included Master the fundamentals of ML and take the first step towards a career in AI! In today’s rapidly evolving world, machine learning (ML) is no longer just for researchers or data scientists. From personalized recommendations on streaming platforms to fraud detection in banking, ML powers many aspects of our daily lives. As industries increasingly adopt AI-driven solutions, learning machine learning has become a valuable skill. Yet, many find the subject overwhelming, often intimidated by its mathematical complexity. That’s where Machine Learning Essentials You Always Wanted to Know (Machine Learning Essentials) comes in. This beginner-friendly guide offers a structured, step-by-step approach to understanding machine learning concepts without unnecessary jargon. Whether you are a student, a professional looking to transition into AI, or simply curious about how machines learn, this book provides a clear and practical roadmap to mastering ML. Authored by Dhairya Parikh, an experienced data engineer who returned to academia to refine his expertise, this book bridges the gap between theory and real-world application. It simplifies the core concepts of ML, breaking them down into digestible explanations paired with hands-on coding exercises to help you apply what you learn. What You’ll Learn: · The fundamentals of machine learning and how it powers modern technology · The three key types of ML—Supervised, Unsupervised, and Reinforcement Learning · How to combine algorithms, data, and models to develop AI-driven solutions · Practical coding techniques to build and implement machine learning models Part of Vibrant Publishers’ Self-Learning Management Series, this book serves as a valuable guide for building machine learning skills, enhancing your expertise, and advancing your career in AI and data science.
Machine Learning Essentials And Applications
DOWNLOAD
Author : Mrs. N. Jayasri
language : en
Publisher: RK Publication
Release Date : 2024-07-27
Machine Learning Essentials And Applications written by Mrs. N. Jayasri and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-27 with Computers categories.
Machine Learning Essentials and Applications a comprehensive of machine learning's core principles, methodologies, and real-world applications. This book is designed for both beginners and professionals, covering essential topics like supervised and unsupervised learning, neural networks, and deep learning. With clear explanations and practical examples, it connects theory to practice, showcasing machine learning’s impact across industries such as healthcare, finance, and technology. Ideal for readers seeking foundational knowledge and insights into the transformative potential of machine learning in various fields.
R Machine Learning Essentials
DOWNLOAD
Author : Michele Usuelli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-11-28
R Machine Learning Essentials written by Michele Usuelli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-28 with Computers categories.
If you want to learn how to develop effective machine learning solutions to your business problems in R, this book is for you. It would be helpful to have a bit of familiarity with basic object-oriented programming concepts, but no prior experience is required.
Neural Network Fundamentals With Graphs Algorithms And Applications
DOWNLOAD
Author : Nirmal K. Bose
language : en
Publisher: McGraw-Hill Companies
Release Date : 1996
Neural Network Fundamentals With Graphs Algorithms And Applications written by Nirmal K. Bose and has been published by McGraw-Hill Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
Deep Learning With Jax
DOWNLOAD
Author : Grigory Sapunov
language : en
Publisher: Simon and Schuster
Release Date : 2024-12-03
Deep Learning With Jax written by Grigory Sapunov and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Computers categories.
Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. In Deep Learning with JAX you will learn how to: • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax • Leverage libraries and modules from the JAX ecosystem Deep Learning with JAX is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. About the technology Google’s JAX offers a fresh vision for deep learning. This powerful library gives you fine control over low level processes like gradient calculations, delivering fast and efficient model training and inference, especially on large datasets. JAX has transformed how research scientists approach deep learning. Now boasting a robust ecosystem of tools and libraries, JAX makes evolutionary computations, federated learning, and other performance-sensitive tasks approachable for all types of applications. About the book Deep Learning with JAX teaches you to build effective neural networks with JAX. In this example-rich book, you’ll discover how JAX’s unique features help you tackle important deep learning performance challenges, like distributing computations across a cluster of TPUs. You’ll put the library into action as you create an image classification tool, an image filter application, and other realistic projects. The nicely-annotated code listings demonstrate how JAX’s functional programming mindset improves composability and parallelization. What's inside • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax About the reader For intermediate Python programmers who are familiar with deep learning. About the author Grigory Sapunov holds a Ph.D. in artificial intelligence and is a Google Developer Expert in Machine Learning. The technical editor on this book was Nicholas McGreivy. Table of Contents Part 1 1 When and why to use JAX 2 Your first program in JAX Part 2 3 Working with arrays 4 Calculating gradients 5 Compiling your code 6 Vectorizing your code 7 Parallelizing your computations 8 Using tensor sharding 9 Random numbers in JAX 10 Working with pytrees Part 3 11 Higher-level neural network libraries 12 Other members of the JAX ecosystem A Installing JAX B Using Google Colab C Using Google Cloud TPUs D Experimental parallelization
A Survey Of Machine Learning Models For Prediabetes Screening
DOWNLOAD
Author : Amos Olwendo
language : en
Publisher: GRIN Verlag
Release Date : 2025-03-13
A Survey Of Machine Learning Models For Prediabetes Screening written by Amos Olwendo and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Computers categories.
Scientific Study from the year 2025 in the subject Communications - Multimedia, Internet, New Technologies, grade: 18.0, Kenyatta University, language: English, abstract: Diabetes is gradually becoming a global challenge owing to the gradual increase in the number of cases of Type 2 diabetes mellitus (T2DM). T2DM is characterized as a state of hyperglycaemia due to abnormal control of insulin levels that eventually affects metabolism. This study aimed to review articles that implement machine learning methods to identify suitable risk factors for prediabetes. The study adopted the preferred reporting items for systematic review (PRISMA) protocol and research questions were formulated by the identification of synonyms and related terms "predictors and prediabetes and machine learning" from PubMed and Google scholar. Both observational and interventional original articles that were published between 2018 and 2023 were included in this study. Eligibility for inclusion was determined by scanning the article title, abstract, and study methodology section.
F For Machine Learning Essentials
DOWNLOAD
Author : Sudipta Mukherjee
language : en
Publisher: Packt Publishing
Release Date : 2016-02-25
F For Machine Learning Essentials written by Sudipta Mukherjee and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-25 with Computers categories.
Get up and running with machine learning with F# in a fun and functional wayAbout This Book- Design algorithms in F# to tackle complex computing problems- Be a proficient F# data scientist using this simple-to-follow guide- Solve real-world, data-related problems with robust statistical models, built for a range of datasetsWho This Book Is ForIf you are a C# or an F# developer who now wants to explore the area of machine learning, then this book is for you. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage.What You Will Learn- Use F# to find patterns through raw data- Build a set of classification systems using Accord.NET, Weka, and F#- Run machine learning jobs on the Cloud with MBrace- Perform mathematical operations on matrices and vectors using Math.NET- Use a recommender system for your own problem domain- Identify tourist spots across the globe using inputs from the user with decision tree algorithmsIn DetailThe F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs.If you want to learn how to use F# to build machine learning systems, then this is the book you want.Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data.Style and approachThis book is a fast-paced tutorial guide that uses hands-on examples to explain real-world applications of machine learning. Using practical examples, the book will explore several machine learning techniques and also describe how you can use F# to build machine learning systems.
Machine Learning And Data Science
DOWNLOAD
Author : Prateek Agrawal
language : en
Publisher: John Wiley & Sons
Release Date : 2022-08-09
Machine Learning And Data Science written by Prateek Agrawal 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 2022-08-09 with Computers categories.
MACHINE LEARNING AND DATA SCIENCE Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia. Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms. These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.
Machine Learning Essentials
DOWNLOAD
Author : Barrett Williams
language : en
Publisher: Barrett Williams
Release Date : 2024-12-01
Machine Learning Essentials written by Barrett Williams and has been published by Barrett Williams this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-01 with Computers categories.
Unlock the potential of data and step into the future with "Machine Learning Essentials," the ultimate guide for mastering predictive analytics. Whether you're a newcomer or looking to deepen your understanding, this comprehensive eBook is designed to equip you with the tools and knowledge you need to excel in the dynamic field of machine learning. Begin your journey by exploring the foundational principles of machine learning and its transformative impact on predictive analytics. Learn how to expertly prepare and engineer your data, selecting and extracting the features that matter most. Dive into handling imbalanced data with precision, ensuring your models are accurate and robust. Discover the power of classification algorithms with insights into decision trees, random forests, support vector machines, and logistic regression. Transition smoothly into regression techniques, harnessing the potential of linear, polynomial, and regularization methods. Explore the realm of unsupervised learning to unveil predictive insights using clustering methods, dimensionality reduction techniques, and anomaly detection. Evaluate model performance like a pro with cross-validation strategies, confusion matrices, and ROC/AUC metrics. Venture into neural networks, unlocking the basics of their architecture, activation functions, and training methodologies. Delve into advanced deep learning topics with convolutional, recurrent, and generative adversarial networks. Optimize your models through hyperparameter tuning and feature importance analysis, selecting the most effective techniques for your goals. Gain practical business insights by implementing machine learning in marketing analytics, risk assessment, and fraud detection. Familiarize yourself with essential tools and libraries like Python, Scikit-Learn, TensorFlow, and PyTorch. Learn from real-world case studies in retail, healthcare, and finance, and tackle ethical considerations in algorithmic bias and data security. Prepare for the future with insights into automated machine learning, IoT, and evolving AI technologies. Take practical steps to launch your machine learning journey, setting up your environment and connecting with a vibrant community of practitioners. "Machine Learning Essentials" is your all-in-one resource for gaining actionable expertise and driving innovation in today's data-driven world. Start your learning adventure today and transform your career with this indispensable guide.