Machine Learning A Comprehensive Overview Of Algorithms And Techniques
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| Author | : Xin-She Yang |
| Publisher | : Academic Press |
| Total Pages | : 190 |
| Release | : 2019-06-17 |
| Genre | : Mathematics |
| ISBN | : 0128172177 |
Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages
| Author | : Shai Shalev-Shwartz |
| Publisher | : Cambridge University Press |
| Total Pages | : 415 |
| Release | : 2014-05-19 |
| Genre | : Computers |
| ISBN | : 1107057132 |
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
| Author | : Dr. T AASIF AHMED |
| Publisher | : Xoffencerpublication |
| Total Pages | : 259 |
| Release | : 2023-05-23 |
| Genre | : Computers |
| ISBN | : 9394707875 |
The quantity of data that we have access to is mind-boggling. It would seem that the volume of information in the world, as well as in our own lives, is only going to continue to increase, and there does not appear to be any end in sight for this trend. The ever-present nature of personal computers has made it an absurdly easy process to store things that, in the past, we would have thrown away. This has resulted in a significant reduction in the amount of waste that we produce. It is all too easy for us to put off making decisions on what to do with all of this information since affordable multigigabyte drives are widely accessible. Instead of making these decisions, we just buy another disk and keep everything. Electronics are present in almost every location, and these devices are always monitoring and documenting our actions. For example, they keep track of the items we buy at the grocery store, our typical purchasing habits, and even when we enter and exit a building. When we swipe our way across the planet, a record is added to a database for every consecutive swipe that we make. The World Wide Web not only overwhelms us with information but also keeps a record of every choice that each of us makes. Aside from the fact that all of them are only personal choices, their commercial and industrial analogues may be found in a variety of guises all over the world. Everyone in this room can agree to the reality that there is a growing understanding gap between the data that is being produced and what we are able to do with that data. The amount of data that people are able to grasp is falling at an alarming rate, and this decline is proportional to the relentless growth in the amount of data. There is information, knowledge that may be really vital, that is latent among all of this data, but it is seldom made explicit or used in any way. The process of recognizing patterns in data is explored in depth throughout the pages of this book. This is not something that simply happened a short while ago. People have been searching for patterns in various types of data ever since the dawn of human history. People who hunt animals search for patterns in the migration patterns of the creatures they are hunting, farmers look for patterns in how their crops grow, politicians look for patterns in how people feel, and lovers look for patterns in how their partners respond
| Author | : |
| Publisher | : Xoffencerpublication |
| Total Pages | : 217 |
| Release | : 2023-07-04 |
| Genre | : Computers |
| ISBN | : 8196401833 |
The field of artificial intelligence (AI) and computer science known as machine learning is focused on the use of data and algorithms to simulate the method in which people learn, with the goal of continuously improving the accuracy of the simulation. Machine learning has a long and illustrious history at IBM. As a result of Arthur Samuel's study (PDF, 481 KB) (link lives outside of IBM) revolving around the game of checkers, the phrase "machine learning" is often regarded as having been first used by a member of IBM's staff. Robert Nealey, who fancied himself the world's best player at checkers, challenged an IBM 7094 computer to a match in 1962 and was defeated by the machine. This accomplishment may appear little when weighed against what is now possible, yet it is recognized as a significant turning point in the development of artificial intelligence. In the past few of decades, technical advancements in storage and processing capacity have made it possible for a number of novel products based on machine learning to become available. Some examples of these products are the recommendation engine used by Netflix and autonomous vehicles. The rapidly developing discipline of data science has an essential subfield known as machine learning. Data mining initiatives involve the training of algorithms to create classifications or predictions, as well as the discovery of critical insights, through the utilization of statistical methodologies. The subsequent decisions made inside applications and enterprises are influenced by these insights, which should ideally have an effect on key growth indicators. It is expected that there will be a greater need for data scientists in the industry as big data continues to develop and flourish. They will be expected to assist in determining the business questions that are the most pertinent, as well as the data necessary to answer those questions. Frameworks that speed up the construction of solutions are usually used while developing machine learning algorithms. Some examples of such frameworks are TensorFlow and PyTorch
| Author | : Giuseppe Bonaccorso |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 352 |
| Release | : 2017-07-24 |
| Genre | : Computers |
| ISBN | : 1785884514 |
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.
| Author | : Gopinath Rebala |
| Publisher | : Springer |
| Total Pages | : 275 |
| Release | : 2019-05-07 |
| Genre | : Technology & Engineering |
| ISBN | : 3030157296 |
Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.
| Author | : Mettu Srinivas |
| Publisher | : John Wiley & Sons |
| Total Pages | : 372 |
| Release | : 2021-08-10 |
| Genre | : Computers |
| ISBN | : 1119769248 |
Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.
| Author | : Mohammed J. Zaki |
| Publisher | : Cambridge University Press |
| Total Pages | : 779 |
| Release | : 2020-01-30 |
| Genre | : Business & Economics |
| ISBN | : 1108473989 |
New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.
| Author | : Abdulhamit Subasi |
| Publisher | : Academic Press |
| Total Pages | : 536 |
| Release | : 2020-06-05 |
| Genre | : Computers |
| ISBN | : 0128213809 |
Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. - Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas - Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data - Explores important classification and regression algorithms as well as other machine learning techniques - Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features
| Author | : Jason Brownlee |
| Publisher | : Machine Learning Mastery |
| Total Pages | : 162 |
| Release | : 2016-03-04 |
| Genre | : Computers |
| ISBN | : |
You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.
