Practical Machine Learning Applications
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PRACTICAL MACHINE LEARNING APPLICATIONS
Author: Dr. Sachin R. Jadhav
language: en
Publisher: Xoffencerpublication
Release Date: 2023-09-18
It is not feasible to arrive at an accurate estimate of the total quantity of knowledge that has been accumulated as a direct consequence of man's activity. Every single day, millions of new tuples are added to the databases, and each of those tuples represents an observation, an experience that can be learned from it, and a situation that may occur again in the future in a way that is comparable to the one it happened in when it was first observed. As human beings, we have the innate capacity to gain knowledge from our experiences, and this is something that occurs constantly throughout our lives. Nevertheless, what does place when the number of occurrences to which we are exposed is more than our capacity to comprehend each of them? What would happen if a fact were to be repeated millions of times, but it would never happen precisely the same way again? What would the results be? What kind of outcomes may we anticipate? It is a subfield of artificial intelligence that focuses on learning from experience, or, to be more specific, the process of automatically extracting implicit knowledge from information that is stored in the form of data. This subfield was named after the concept of learning from experience. Machine learning, which is sometimes shortened as ML and referred to in certain contexts as ML, is sometimes referred to simply as ML. In this study, we will investigate two problems that have been solved in the actual world of business by using machine learning. These problems were faced by companies throughout the globe. Companies were tasked with overcoming both of these obstacles. The first of these responsibilities is to provide an accurate forecast of the final product quality that will be supplied by an oil and gas refinery, which is discussed in Section 2. The second component is a model that, as will be covered in Section 3, may be used in order to acquire an estimate of the amount of wear and tear that will be experienced by a collection of micro gas turbines. This will be accomplished by calculating the amount of wear and tear that can be expected from the collection of micro gas turbines. In the phrase that follows, we will talk about the theoretical components that are essential for the creation of our solutions. An explanation of the ML approaches that we have used may be found in Section 1.1 for any reader who is interested in reading it and would want to read it.
Practical Machine Learning
Author: Sunila Gollapudi
language: en
Publisher: Packt Publishing Ltd
Release Date: 2016-01-30
Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn Implement a wide range of algorithms and techniques for tackling complex data Get to grips with some of the most powerful languages in data science, including R, Python, and Julia Harness the capabilities of Spark and Hadoop to manage and process data successfully Apply the appropriate machine learning technique to address real-world problems Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data. This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naive Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies. Style and approach A practical data science tutorial designed to give you an insight into the practical application of machine learning, this book takes you through complex concepts and tasks in an accessible way. Featuring information on a wide range of data science techniques, Practical Machine Learning is a comprehensive data science resource.
Practical Machine Learning in R
Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.