Data Science and Machine Learning

Data Science and Machine Learning
Author :
Publisher : CRC Press
Total Pages : 538
Release :
ISBN-10 : 9781000730777
ISBN-13 : 1000730778
Rating : 4/5 (77 Downloads)

Synopsis Data Science and Machine Learning by : Dirk P. Kroese

Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Machine Learning and Data Science Blueprints for Finance

Machine Learning and Data Science Blueprints for Finance
Author :
Publisher : "O'Reilly Media, Inc."
Total Pages : 432
Release :
ISBN-10 : 9781492073000
ISBN-13 : 1492073008
Rating : 4/5 (00 Downloads)

Synopsis Machine Learning and Data Science Blueprints for Finance by : Hariom Tatsat

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

Machine Learning
Author :
Publisher : MIT Press
Total Pages : 1102
Release :
ISBN-10 : 9780262018029
ISBN-13 : 0262018020
Rating : 4/5 (29 Downloads)

Synopsis Machine Learning by : Kevin P. Murphy

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Encyclopedia of Data Science and Machine Learning

Encyclopedia of Data Science and Machine Learning
Author :
Publisher : IGI Global
Total Pages : 3296
Release :
ISBN-10 : 9781799892212
ISBN-13 : 1799892212
Rating : 4/5 (12 Downloads)

Synopsis Encyclopedia of Data Science and Machine Learning by : Wang, John

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

Machine Learning and Data Science

Machine Learning and Data Science
Author :
Publisher :
Total Pages : 0
Release :
ISBN-10 : 1634620968
ISBN-13 : 9781634620963
Rating : 4/5 (68 Downloads)

Synopsis Machine Learning and Data Science by : Daniel D. Gutierrez

This book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains.

Data-Driven Science and Engineering

Data-Driven Science and Engineering
Author :
Publisher : Cambridge University Press
Total Pages : 615
Release :
ISBN-10 : 9781009098489
ISBN-13 : 1009098489
Rating : 4/5 (89 Downloads)

Synopsis Data-Driven Science and Engineering by : Steven L. Brunton

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Introducing Data Science

Introducing Data Science
Author :
Publisher : Simon and Schuster
Total Pages : 475
Release :
ISBN-10 : 9781638352495
ISBN-13 : 1638352496
Rating : 4/5 (95 Downloads)

Synopsis Introducing Data Science by : Davy Cielen

Summary Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started. About the Book Introducing Data ScienceIntroducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. What’s Inside Handling large data Introduction to machine learning Using Python to work with data Writing data science algorithms About the Reader This book assumes you're comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required. About the Authors Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors. Table of Contents Data science in a big data world The data science process Machine learning Handling large data on a single computer First steps in big data Join the NoSQL movement The rise of graph databases Text mining and text analytics Data visualization to the end user

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
Author :
Publisher : Springer
Total Pages : 0
Release :
ISBN-10 : 1493938436
ISBN-13 : 9781493938438
Rating : 4/5 (36 Downloads)

Synopsis Pattern Recognition and Machine Learning by : Christopher M. Bishop

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Machine Learning and Data Science in the Power Generation Industry

Machine Learning and Data Science in the Power Generation Industry
Author :
Publisher : Elsevier
Total Pages : 276
Release :
ISBN-10 : 9780128226001
ISBN-13 : 0128226005
Rating : 4/5 (01 Downloads)

Synopsis Machine Learning and Data Science in the Power Generation Industry by : Patrick Bangert

Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. - Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful - Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them - Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems - Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls

Machine Learning in Production

Machine Learning in Production
Author :
Publisher : Addison-Wesley Professional
Total Pages : 465
Release :
ISBN-10 : 9780134116563
ISBN-13 : 0134116569
Rating : 4/5 (63 Downloads)

Synopsis Machine Learning in Production by : Andrew Kelleher

Foundational Hands-On Skills for Succeeding with Real Data Science Projects This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings. –From the Foreword by Paul Dix, series editor Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments. Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. Leverage agile principles to maximize development efficiency in production projects Learn from practical Python code examples and visualizations that bring essential algorithmic concepts to life Start with simple heuristics and improve them as your data pipeline matures Avoid bad conclusions by implementing foundational error analysis techniques Communicate your results with basic data visualization techniques Master basic machine learning techniques, starting with linear regression and random forests Perform classification and clustering on both vector and graph data Learn the basics of graphical models and Bayesian inference Understand correlation and causation in machine learning models Explore overfitting, model capacity, and other advanced machine learning techniques Make informed architectural decisions about storage, data transfer, computation, and communication Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.