Algorithms For Data Science
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Algorithms For Data Science
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Author : Brian Steele
language : en
Publisher: Springer
Release Date : 2016-12-25
Algorithms For Data Science written by Brian Steele and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-25 with Computers categories.
This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
Data Science
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Author : Qurban A Memon
language : en
Publisher: CRC Press
Release Date : 2019-09-26
Data Science written by Qurban A Memon and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-26 with Computers categories.
The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: • Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. • Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. • Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science.
Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing
Release Date : 2018-08-30
Machine Learning Algorithms written by Giuseppe Bonaccorso and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-30 with Computers categories.
An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book Description Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn Study feature selection and the 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 (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is for Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book.
Foundations Of Data Science
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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23
Foundations Of Data Science written by Avrim Blum and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-23 with Computers categories.
Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.
Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher:
Release Date : 2017
Machine Learning Algorithms written by Giuseppe Bonaccorso and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Computer algorithms categories.
Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-30
Machine Learning Algorithms written by Giuseppe Bonaccorso and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-30 with Mathematics categories.
An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book Description Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn Study feature selection and the 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 (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is for Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book.
Python Data Science Essentials
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Author : Alberto Boschetti
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-10-28
Python Data Science Essentials written by Alberto Boschetti and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-28 with Computers categories.
Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Fundamentals Of Data Science
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Author : Samuel Burns
language : en
Publisher:
Release Date : 2019-09-17
Fundamentals Of Data Science written by Samuel Burns and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-17 with Big data categories.
"This book is for students or anyone, with limited or no prior programming, statistics, and data analytics knowledge. This short guide is ideal for absolute beginners, or anyone who wants to acquire a basic working knowledge of data science. It is an excellent guide if you want to learn about the principals of data science from scratch, in just a few hours. The author discussed everything that you need to know about data science. First, you are guided to learn the meaning of data science. The history of data science has been discussed to help you know how people came to realize that data is a rich source of knowledge and intelligence. The theories underlying data science have been discussed. Examples include decision and estimation theories. The author discussed the various machine learning algorithms used in data science and the various steps one has to undergo when performing data science tasks, from data collection to data presentation and visualization. The author helps you to know the various ways through which you can apply data science in your business for increased profits. A simple language has been used to ensure ease of understanding, especially for beginners." --
Mathematics Of Data Science
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Author : Daniela Calvetti
language : en
Publisher: SIAM
Release Date : 2020-11-20
Mathematics Of Data Science written by Daniela Calvetti and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-20 with Mathematics categories.
This textbook provides a solid mathematical basis for understanding popular data science algorithms for clustering and classification and shows that an in-depth understanding of the mathematics powering these algorithms gives insight into the underlying data. It presents a step-by-step derivation of these algorithms, outlining their implementation from scratch in a computationally sound way. Mathematics of Data Science: A Computational Approach to Clustering and Classification proposes different ways of visualizing high-dimensional data to unveil hidden internal structures, and nearly every chapter includes graphical explanations and computed examples using publicly available data sets to highlight similarities and differences among the algorithms. This self-contained book is geared toward advanced undergraduate and beginning graduate students in the mathematical sciences, engineering, and computer science and can be used as the main text in a semester course. Researchers in any application area where data science methods are used will also find the book of interest. No advanced mathematical or statistical background is assumed.
Data Science
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Author : Gyanendra K. Verma
language : en
Publisher:
Release Date : 2021
Data Science written by Gyanendra K. Verma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.