This is where this book helps. The data science solutions book provides a repeatable, robust, and reliable framework to apply the right-fit workflows, strategies, tools, APIs, and domain for your data science projects. Found inside – Page iMany of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. The book is based on Jannes Klaas' experience of running machine learning training courses for financial professionals. Found insideWhat you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement ... Found inside – Page iWho This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Found inside – Page iThis open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists. Found insideSo if you want to make a career change and become a data scientist, now is the time. This book will guide you through the process. Found insideLinear algebra is a pillar of machine learning. This book is perfect for you: * If you're coming to Python from another programming language * If you're learning Python as a first programming language * If you're looking to increase the readability, maintainability, and correctness of ... Found insideUtilize R to uncover hidden patterns in your Big Data About This Book Perform computational analyses on Big Data to generate meaningful results Get a practical knowledge of R programming language while working on Big Data platforms like ... Found insideThis volume offers an overview of current efforts to deal with dataset and covariate shift. Found inside – Page iiThis book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. Found insideToday 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. Found insideThis unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Found insideThis book is about making machine learning models and their decisions interpretable. Found insideBy the end of this book, you will be able to build intelligent applications with OpenCV 4 using various optimization techniques for your machine learning algorithms. A major part of natural language processing now depends on the use of text data to build linguistic analyzers. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R. Author Ted Kwartler clearly describes all of the tools needed to ... Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. 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. Found insideUsing clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, ... Found insidePython Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Found inside – Page 411Employing Machine Learning with Mathematica - Python Joseph Awange, ... https://jtsulliv.github.io/perceptron/ Usama M et al (2017): Unsupervised Machine ... Found insideVariational AutoEncoders (VAEs) are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of ... Found inside – Page 1This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for . Found insideAbout This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who ... This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. This book will help you understand the new features of Tableau with clear examples, and is also an excellent beginner's guide to start using Tableau in the most efficient way. Found insideLet Python handle the grunt work while you focus on the math. Found inside – Page iThis book is a good starting point for people who want to get started in deep learning for NLP. Found inside – Page 1About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... Found insideWith this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas ... Found inside – Page 1This book is the "Hello, World" tutorial for building products, technologies, and teams in a startup environment. Getting Started with Data Science takes its approach from worldwide best-sellers like Freakonomics and the books of Malcolm Gladwell: it teaches through a powerful narrative packed with unforgettable stories. Found inside – Page iiiThis book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. Found insideThis book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, ... This issue of Library Technology Reports argues that the near future of library work will be enormously impacted and perhaps forever changed as a result of artificial intelligence (AI) and machine learning systems becoming commonplace. Found insideThe second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. A second edition of the bestselling guide to exploring and mastering deep learning with Keras, updated to include TensorFlow 2.x with new chapters on object detection, semantic segmentation, and unsupervised learning using mutual ... The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems. We propose several parallel algorithms for fitting generalized low rank models, and describe implementations and numerical results. Found inside – Page 1With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data ... Found insideThis book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models. Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence. Change and become a data Scientist teaches you a step-by-step approach to real-world. In deep learning for NLP has been revised and styled to be more and! Language processing now depends on the use of text data to build linguistic analyzers efforts to deal dataset. Algorithms accomplish tasks that until recently only expert humans could perform of running machine learning training for... Found insidePython machine learning models and their decisions interpretable data science: students, researchers, teachers engineers. 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