Advances In Machine Learning And Data Science

Advances In Machine Learning And Data Science Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Advances In Machine Learning And Data Science book. This book definitely worth reading, it is an incredibly well-written.

Advances in Machine Learning and Data Mining for Astronomy

Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Advances in Machine Learning and Data Mining for Astronomy Book PDF

Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publisher : CRC Press
Page : 746 pages
File Size : 50,9 Mb
Release : 2012-03-29
Category : Computers
ISBN : 9781439841730

Get Book

Advances in Machine Learning and Data Mining for Astronomy by Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava Pdf

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Advances in Machine Learning and Data Science

Damodar Reddy Edla,Pawan Lingras,Venkatanareshbabu K.
Advances in Machine Learning and Data Science Book PDF

Author : Damodar Reddy Edla,Pawan Lingras,Venkatanareshbabu K.
Publisher : Springer
Page : 380 pages
File Size : 43,8 Mb
Release : 2018-05-16
Category : Technology & Engineering
ISBN : 9789811085697

Get Book

Advances in Machine Learning and Data Science by Damodar Reddy Edla,Pawan Lingras,Venkatanareshbabu K. Pdf

The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine Learning and Data Science (LAMDA 2017). The 37 regular papers presented in this volume were carefully reviewed and selected from 123 submissions. These days we find many computer programs that exhibit various useful learning methods and commercial applications. Goal of machine learning is to develop computer programs that can learn from experience. Machine learning involves knowledge from various disciplines like, statistics, information theory, artificial intelligence, computational complexity, cognitive science and biology. For problems like handwriting recognition, algorithms that are based on machine learning out perform all other approaches. Both machine learning and data science are interrelated. Data science is an umbrella term to be used for techniques that clean data and extract useful information from data. In field of data science, machine learning algorithms are used frequently to identify valuable knowledge from commercial databases containing records of different industries, financial transactions, medical records, etc. The main objective of this book is to provide an overview on latest advancements in the field of machine learning and data science, with solutions to problems in field of image, video, data and graph processing, pattern recognition, data structuring, data clustering, pattern mining, association rule based approaches, feature extraction techniques, neural networks, bio inspired learning and various machine learning algorithms.

Advances in Financial Machine Learning

Marcos Lopez de Prado
Advances in Financial Machine Learning Book PDF

Author : Marcos Lopez de Prado
Publisher : John Wiley & Sons
Page : 400 pages
File Size : 55,5 Mb
Release : 2018-01-23
Category : Business & Economics
ISBN : 9781119482116

Get Book

Advances in Financial Machine Learning by Marcos Lopez de Prado Pdf

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Machine Learning and Data Science Blueprints for Finance

Hariom Tatsat,Sahil Puri,Brad Lookabaugh
Machine Learning and Data Science Blueprints for Finance Book PDF

Author : Hariom Tatsat,Sahil Puri,Brad Lookabaugh
Publisher : "O'Reilly Media, Inc."
Page : 432 pages
File Size : 52,9 Mb
Release : 2020-10-01
Category : Computers
ISBN : 9781492073000

Get Book

Machine Learning and Data Science Blueprints for Finance by Hariom Tatsat,Sahil Puri,Brad Lookabaugh Pdf

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Machine Learning Paradigms

Maria Virvou,Efthimios Alepis,George A. Tsihrintzis,Lakhmi C. Jain
Machine Learning Paradigms Book PDF

Author : Maria Virvou,Efthimios Alepis,George A. Tsihrintzis,Lakhmi C. Jain
Publisher : Springer
Page : 223 pages
File Size : 53,7 Mb
Release : 2019-03-16
Category : Technology & Engineering
ISBN : 9783030137434

Get Book

Machine Learning Paradigms by Maria Virvou,Efthimios Alepis,George A. Tsihrintzis,Lakhmi C. Jain Pdf

This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.

Advanced Machine Learning with Python

John Hearty
Advanced Machine Learning with Python Book PDF

Author : John Hearty
Publisher : Packt Publishing Ltd
Page : 278 pages
File Size : 46,5 Mb
Release : 2016-07-28
Category : Computers
ISBN : 9781784393830

Get Book

Advanced Machine Learning with Python by John Hearty Pdf

Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.

Advances in Data Science

Ilke Demir,Yifei Lou,Xu Wang,Kathrin Welker
Advances in Data Science Book PDF

Author : Ilke Demir,Yifei Lou,Xu Wang,Kathrin Welker
Publisher : Springer Nature
Page : 374 pages
File Size : 43,8 Mb
Release : 2021-12-03
Category : Mathematics
ISBN : 9783030798918

Get Book

Advances in Data Science by Ilke Demir,Yifei Lou,Xu Wang,Kathrin Welker Pdf

This volume highlights recent advances in data science, including image processing and enhancement on large data, shape analysis and geometry processing in 2D/3D, exploration and understanding of neural networks, and extensions to atypical data types such as social and biological signals. The contributions are based on discussions from two workshops under Association for Women in Mathematics (AWM), namely the second Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place between July 29 and August 2, 2019 at the Institute for Computational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, and the third Women in Shape (WiSh) Research Collaboration Workshop that took place between July 16 and 20, 2018 at Trier University in Robert-Schuman-Haus, Trier, Germany. These submissions, seeded by working groups at the conference, form a valuable source for readers who are interested in ideas and methods developed in interdisciplinary research fields. The book features ideas, methods, and tools developed through a broad range of domains, ranging from theoretical analysis on graph neural networks to applications in health science. It also presents original results tackling real-world problems that often involve complex data analysis on large multi-modal data sources.

Advances in Machine Learning Applications in Software Engineering

Zhang, Du,Tsai, Jeffery J.P.
Advances in Machine Learning Applications in Software Engineering Book PDF

Author : Zhang, Du,Tsai, Jeffery J.P.
Publisher : IGI Global
Page : 498 pages
File Size : 40,5 Mb
Release : 2006-10-31
Category : Computers
ISBN : 9781591409434

Get Book

Advances in Machine Learning Applications in Software Engineering by Zhang, Du,Tsai, Jeffery J.P. Pdf

"This book provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. It depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality while offering readers suggestions by proposing future work in this emerging research field"--Provided by publisher.

Advances in Financial Machine Learning

Marcos Lopez de Prado
Advances in Financial Machine Learning Book PDF

Author : Marcos Lopez de Prado
Publisher : John Wiley & Sons
Page : 395 pages
File Size : 51,6 Mb
Release : 2018-02-02
Category : Business & Economics
ISBN : 9781119482109

Get Book

Advances in Financial Machine Learning by Marcos Lopez de Prado Pdf

Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Advanced Deep Learning Applications in Big Data Analytics

Bouarara, Hadj Ahmed
Advanced Deep Learning Applications in Big Data Analytics Book PDF

Author : Bouarara, Hadj Ahmed
Publisher : IGI Global
Page : 351 pages
File Size : 42,6 Mb
Release : 2020-10-16
Category : Computers
ISBN : 9781799827931

Get Book

Advanced Deep Learning Applications in Big Data Analytics by Bouarara, Hadj Ahmed Pdf

Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.

Advances in Machine Learning for Big Data Analysis

Satchidananda Dehuri,Yen-Wei Chen
Advances in Machine Learning for Big Data Analysis Book PDF

Author : Satchidananda Dehuri,Yen-Wei Chen
Publisher : Springer Nature
Page : 254 pages
File Size : 44,7 Mb
Release : 2022-02-24
Category : Technology & Engineering
ISBN : 9789811689307

Get Book

Advances in Machine Learning for Big Data Analysis by Satchidananda Dehuri,Yen-Wei Chen Pdf

This book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems. In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems.

Grokking Deep Learning

Andrew W. Trask
Grokking Deep Learning Book PDF

Author : Andrew W. Trask
Publisher : Simon and Schuster
Page : 475 pages
File Size : 52,5 Mb
Release : 2019-01-23
Category : Computers
ISBN : 9781638357209

Get Book

Grokking Deep Learning by Andrew W. Trask Pdf

Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide

Machine Learning and Data Science

Prateek Agrawal,Charu Gupta,Anand Sharma,Vishu Madaan,Nisheeth Joshi
Machine Learning and Data Science Book PDF

Author : Prateek Agrawal,Charu Gupta,Anand Sharma,Vishu Madaan,Nisheeth Joshi
Publisher : John Wiley & Sons
Page : 276 pages
File Size : 47,8 Mb
Release : 2022-08-09
Category : Computers
ISBN : 9781119775614

Get Book

Machine Learning and Data Science by Prateek Agrawal,Charu Gupta,Anand Sharma,Vishu Madaan,Nisheeth Joshi Pdf

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 Paradigms

George A. Tsihrintzis,Dionisios N. Sotiropoulos,Lakhmi C. Jain
Machine Learning Paradigms Book PDF

Author : George A. Tsihrintzis,Dionisios N. Sotiropoulos,Lakhmi C. Jain
Publisher : Springer
Page : 370 pages
File Size : 47,7 Mb
Release : 2018-07-03
Category : Technology & Engineering
ISBN : 9783319940304

Get Book

Machine Learning Paradigms by George A. Tsihrintzis,Dionisios N. Sotiropoulos,Lakhmi C. Jain Pdf

This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences. Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics. This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.

Advances in Artificial Intelligence, Computation, and Data Science

Tuan D. Pham,Hong Yan,Muhammad W. Ashraf,Folke Sjöberg
Advances in Artificial Intelligence Computation and Data Science Book PDF

Author : Tuan D. Pham,Hong Yan,Muhammad W. Ashraf,Folke Sjöberg
Publisher : Springer
Page : 0 pages
File Size : 48,7 Mb
Release : 2022-07-14
Category : Science
ISBN : 3030699536

Get Book

Advances in Artificial Intelligence, Computation, and Data Science by Tuan D. Pham,Hong Yan,Muhammad W. Ashraf,Folke Sjöberg Pdf

Artificial intelligence (AI) has become pervasive in most areas of research and applications. While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity—in both time and memory requirements—for machine learning in large datasets. Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society. This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit. Features: Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistry Provides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data science Reports on applications in medicine and physiology, including cancer, neuroscience, and digital pathology Examines applications in life science, including systems biology, biochemistry, and even food technology This unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education.