Targeted Learning in Data Science

Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists.

Author: Mark J. van der Laan

Publisher: Springer

ISBN: 9783319653044

Category: Mathematics

Page: 640

View: 336

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This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.
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Targeted Learning

This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data.

Author: Mark J. van der Laan

Publisher: Springer Science & Business Media

ISBN: 1441997822

Category: Mathematics

Page: 628

View: 193

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The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.
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Handbook of Big Data

2011. Targeted Learning: Causal Inference for Observational and Experimental
Data. Springer Series in Statistics. New York: Springer. van der Laan, M.J.,
R.J.C.M. Starmans. 2014. Entering the Era of Data Science: Targeted Learning
and the ...

Author: Peter Bühlmann

Publisher: CRC Press

ISBN: 9781482249088

Category: Business & Economics

Page: 464

View: 703

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Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical and computing ideas that can be readily applied in research and practice. Offering balanced coverage of methodology, theory, and applications, this handbook: Describes modern, scalable approaches for analyzing increasingly large datasets Defines the underlying concepts of the available analytical tools and techniques Details intercommunity advances in computational statistics and machine learning Handbook of Big Data also identifies areas in need of further development, encouraging greater communication and collaboration between researchers in big data sub-specialties such as genomics, computational biology, and finance.
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Statistics for Health Data Science

The field of data science is multidisciplinary with considerable overlap with
computer science and machine learning. ... post-selection inference and targeted
learning [10–13], which address inference based on data-adaptive models and
aim ...

Author: Ruth Etzioni

Publisher: Springer Nature

ISBN: 9783030598891

Category:

Page:

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Cross Reality and Data Science in Engineering

Learning outcome test questions This is an online test which the students give
while they are performing the ... So if a student scores well in a task it indicates
that the student has performed well in the learning objectives targeted by the task
.

Author: Michael E. Auer

Publisher: Springer Nature

ISBN: 9783030525750

Category: Computers

Page: 1036

View: 303

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Today, online technologies are at the core of most fields of engineering and society as a whole . This book discusses the fundamentals, applications and lessons learned in the field of online and remote engineering, virtual instrumentation, and other related technologies like Cross Reality, Data Science & Big Data, Internet of Things & Industrial Internet of Things, Industry 4.0, Cyber Security, and M2M & Smart Objects. Since the first Remote Engineering and Virtual Instrumentation (REV) conference in 2004, the event has focused on the use of the Internet for engineering tasks, as well as the related opportunities and challenges. In a globally connected world, interest in online collaboration, teleworking, remote services, and other digital working environments is rapidly increasing. In this context, the REV conferences discuss fundamentals, applications and experiences in the field of Online and Remote Engineering as well as Virtual Instrumentation. Furthermore, the conferences focus on guidelines and new concepts for engineering education in higher and vocational education institutions, including emerging technologies in learning, MOOCs & MOOLs, and open resources. This book presents the proceedings of REV2020 on “Cross Reality and Data Science in Engineering” which was held as the 17th in series of annual events. It was organized in cooperation with the Engineering Education Transformations Institute and the Georgia Informatics Institutes for Research and Education and was held at the College of Engineering at the University of Georgia in Athens (GA), USA, from February 26 to 28, 2020.
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Dynamic Treatment Regimes

Journal of Machine Learning Research 10, 141–158. Sutton ... Adaptive contrast
weighted learning for multi-stage multi-treatment decision-making. ... Targeted
Learning in Data Science: Causal Inference for Complex Longitudinal Studies.

Author: Anastasios A. Tsiatis

Publisher: CRC Press

ISBN: 9781498769785

Category: Mathematics

Page: 602

View: 824

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Dynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a comprehensive introduction to statistical methodology for the evaluation and discovery of dynamic treatment regimes from data. Researchers and graduate students in statistics, data science, and related quantitative disciplines with a background in probability and statistical inference and popular statistical modeling techniques will be prepared for further study of this rapidly evolving field. A dynamic treatment regime is a set of sequential decision rules, each corresponding to a key decision point in a disease or disorder process, where each rule takes as input patient information and returns the treatment option he or she should receive. Thus, a treatment regime formalizes how a clinician synthesizes patient information and selects treatments in practice. Treatment regimes are of obvious relevance to precision medicine, which involves tailoring treatment selection to patient characteristics in an evidence-based way. Of critical importance to precision medicine is estimation of an optimal treatment regime, one that, if used to select treatments for the patient population, would lead to the most beneficial outcome on average. Key methods for estimation of an optimal treatment regime from data are motivated and described in detail. A dedicated companion website presents full accounts of application of the methods using a comprehensive R package developed by the authors. The authors’ website www.dtr-book.com includes updates, corrections, new papers, and links to useful websites.
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Visible learning for teachers

This book: links the biggest ever research project on teaching strategies to practical classroom implementation champions both teacher and student perspectives and contains step by step guidance including lesson preparation, interpreting ...

Author: John Hattie

Publisher:

ISBN: 1003024475

Category: EDUCATION

Page:

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In November 2008, John Hattie's ground-breaking book Visible Learning synthesised the results of more thanfifteen years research involving millions of students and represented the biggest ever collection of evidence-based research into what actually works in schools to improve learning. Visible Learning for Teachers takes the next step and brings those ground breaking concepts to a completely new audience. Written for students, pre-service and in-service teachers, it explains how to apply the principles of Visible Learning to any classroom anywhere in the world. The author offers concise and user-friendly summaries of the most successful interventions and offers practical step-by-step guidance to the successful implementation of visible learning and visible teaching in the classroom. This book: links the biggest ever research project on teaching strategies to practical classroom implementation champions both teacher and student perspectives and contains step by step guidance including lesson preparation, interpreting learning and feedback during the lesson and post lesson follow up offers checklists, exercises, case studies and best practice scenarios to assist in raising achievement includes whole school checklists and advice for school leaders on facilitating visible learning in their institution now includes additional meta-analyses bringing the total cited within the research to over 900 comprehensively covers numerous areas of learning activity including pupil motivation, curriculum, meta-cognitive strategies, behaviour, teaching strategies, and classroom management. Visible Learning for Teachers is a must read for any student or teacher who wants an evidence based answer to the question; 'how do we maximise achievement in our schools?'
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Business Data Science Combining Machine Learning and Economics to Optimize Automate and Accelerate Business Decisions

The ability to deal with this mess and noise is the most important skill you need to
learn to keep from embarrassing yourself as you work and learn with data. In any
analysis, you have targeted unknowns and untargeted unknowns. The former ...

Author: Matt Taddy

Publisher: McGraw Hill Professional

ISBN: 9781260452785

Category: Business & Economics

Page: 384

View: 595

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Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: •Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling•Understand how use ML tools in real world business problems, where causation matters more that correlation•Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.
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Learning from Data Streams

In this algorithm, each node performs PCA, projecting the local data along the
principal components, and applies a known clustering ... Learning localized
alternative cluster ensembles is a related problem recently targeted by
researchers.

Author: João Gama

Publisher: Springer Science & Business Media

ISBN: 9783540736783

Category: Computers

Page: 244

View: 293

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Provides the reader with an overview of stream data processing, including prototype implementations like the Nile system and the TinyOS operating system.
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Evolutionary Computation Machine Learning and Data Mining in Bioinformatics

Understanding disease-related metabolite interactions is a key issue in
computational biology. We apply a modified Bayesian Optimization Algorithm to
targeted metabolomics data from plasma samples of insulin-sensitive and -
resistant ...

Author: Mario Giacobini

Publisher: Springer Science & Business Media

ISBN: 9783642290657

Category: Computers

Page: 255

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This book constitutes the refereed proceedings of the 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012, held in Málaga, Spain, in April 2012 co-located with the Evo* 2012 events. The 15 revised full papers presented together with 8 poster papers were carefully reviewed and selected from numerous submissions. Computational Biology is a wide and varied discipline, incorporating aspects of statistical analysis, data structure and algorithm design, machine learning, and mathematical modeling toward the processing and improved understanding of biological data. Experimentalists now routinely generate new information on such a massive scale that the techniques of computer science are needed to establish any meaningful result. As a consequence, biologists now face the challenges of algorithmic complexity and tractability, and combinatorial explosion when conducting even basic analyses.
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A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care

itories) and has a targeted objective of learning from existing clinical work flows
to improve clinical practice guidelines. ... Legacy clinical data combined with
clinical practice guidelines is a data science methodology that can identify
patterns in ...

Author: Kamran Farooq

Publisher: GRIN Verlag

ISBN: 9783668241985

Category: Computers

Page: 315

View: 406

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Doctoral Thesis / Dissertation from the year 2015 in the subject Computer Science - Miscellaneous, grade: -, University of Stirling (Computing Science and Mathematics), language: English, abstract: Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases.
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An International Comparison of Science Teaching and Learning Further Results from PISA 2006

What do the differences in these approaches mean for students' development of
scientific literacy and science-related attitudes, orientations and interests? With
its main focus on ... Accordingly, the questions are targeted towards teaching and
learning activities that are expected to have a positive effect on ... Data from PISA
2006 enable lesson features to be described from the perspective of the students.

Author: Mareike Kobarg

Publisher: Waxmann Verlag

ISBN: 3830975066

Category: Education

Page: 100

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The OECD Programme for International Student Assessment (PISA) assesses the competencies of 15-year-old students around the world. In 2006, the PISA report focused on the science competencies 15-year-old students developed. The report does not reflect a systematic consideration of science learning environments in schools and their relationship to cognitive and motivational outcomes in terms of scientific literacy. However, in all investigated countries, schools are where young people become familiar with science over an extended period of time. Hence, this book aims to provide detailed information on science teaching and learning in schools in the OECD countries. Data from the PISA 2006 school principals’ and students’ questionnaires is used for the description of science teaching and learning. First, the context of science teaching in schools is described to provide a background for the analyses that follow. Then, the book draws a detailed picture of different components of science teaching relevant for student learning. In addition, international patterns of science teaching and learning are investigated. The investigation focuses on the teaching of scientific enquiry. This focus is chosen because the process of scientific enquiry models the way in which researchers think, and it provides students with ample opportunities to develop science literacy. Further investigations include the effects of different patterns of science teaching on student literacy. The book concludes with implications for policy and practice.
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Software Reviews on File

Page 405 MICROSIFT September 1985 SOCIAL SCIENCE MICROCOMPUTER
REVIEW Fall 1985 " This package could ... 260 - 263 LIFE SCIENCE DATA
BASES [ Science ) AUTHOR : Targeted Learning Corp . , Dick McLeod
CONTENTS : 2 ...

Author:

Publisher:

ISBN: UOM:39015081530209

Category: Computer programs

Page:

View: 674

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Evolutionary Computation Machine Learning and Data Mining in Bioinformatics

Generating Linkage Disequilibrium Patterns in Data Simulations Using
genomeSIMLA Todd L. Edwards1, William S. ... differences between individuals
greatly increases our ability to perform targeted or whole genome association (
WGA) ...

Author: Elena Marchiori

Publisher: Springer Science & Business Media

ISBN: 9783540787563

Category: Computers

Page: 211

View: 757

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This book constitutes the refereed proceedings of the 6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2008, held in Naples, Italy, in March 2008 colocated with the Evo* 2008 events. The 18 revised full papers were carefully reviewed and selected from 63 submissions. EvoBio is the premiere European event for experts in computer science meeting with experts in bioinformatics and the biological sciences, all interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology. Topics addressed by the papers include biomarker discovery, cell simulation and modeling, ecological modeling, uxomics, gene networks, biotechnology, metabolomics, microarray analysis, phylogenetics, protein interactions, proteomics, sequence analysis and alignment, as well as systems biology.
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Past Present and Future of Statistical Science

One can now use a data adaptive selector to select among all these submodel-
specific candidate estimators. This general strategy ... to infinity. 40.4 Super
learning These oracle results for the cross-validation selector 472 Targeted
learning.

Author: Xihong Lin

Publisher: CRC Press

ISBN: 9781482204988

Category: Mathematics

Page: 646

View: 162

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Past, Present, and Future of Statistical Science was commissioned in 2013 by the Committee of Presidents of Statistical Societies (COPSS) to celebrate its 50th anniversary and the International Year of Statistics. COPSS consists of five charter member statistical societies in North America and is best known for sponsoring prestigious awards in statistics, such as the COPSS Presidents’ award. Through the contributions of a distinguished group of 50 statisticians who are past winners of at least one of the five awards sponsored by COPSS, this volume showcases the breadth and vibrancy of statistics, describes current challenges and new opportunities, highlights the exciting future of statistical science, and provides guidance to future generations of statisticians. The book is not only about statistics and science but also about people and their passion for discovery. Distinguished authors present expository articles on a broad spectrum of topics in statistical education, research, and applications. Topics covered include reminiscences and personal reflections on statistical careers, perspectives on the field and profession, thoughts on the discipline and the future of statistical science, and advice for young statisticians. Many of the articles are accessible not only to professional statisticians and graduate students but also to undergraduate students interested in pursuing statistics as a career and to all those who use statistics in solving real-world problems. A consistent theme of all the articles is the passion for statistics enthusiastically shared by the authors. Their success stories inspire, give a sense of statistics as a discipline, and provide a taste of the exhilaration of discovery, success, and professional accomplishment.
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Learning Science Through Computer Games and Simulations

National Research Council, Division of Behavioral and Social Sciences and
Education, Board on Science Education, Committee on Science Learning: ... This
, in turn, could support an increased focus on science process skills and other
learning outcomes that are often targeted by simulations and games but have ...
large amount of data created by student interactions with these learning
environments.

Author: National Research Council

Publisher: National Academies Press

ISBN: 9780309185233

Category: Education

Page: 174

View: 995

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At a time when scientific and technological competence is vital to the nation's future, the weak performance of U.S. students in science reflects the uneven quality of current science education. Although young children come to school with innate curiosity and intuitive ideas about the world around them, science classes rarely tap this potential. Many experts have called for a new approach to science education, based on recent and ongoing research on teaching and learning. In this approach, simulations and games could play a significant role by addressing many goals and mechanisms for learning science: the motivation to learn science, conceptual understanding, science process skills, understanding of the nature of science, scientific discourse and argumentation, and identification with science and science learning. To explore this potential, Learning Science: Computer Games, Simulations, and Education, reviews the available research on learning science through interaction with digital simulations and games. It considers the potential of digital games and simulations to contribute to learning science in schools, in informal out-of-school settings, and everyday life. The book also identifies the areas in which more research and research-based development is needed to fully capitalize on this potential. Learning Science will guide academic researchers; developers, publishers, and entrepreneurs from the digital simulation and gaming community; and education practitioners and policy makers toward the formation of research and development partnerships that will facilitate rich intellectual collaboration. Industry, government agencies and foundations will play a significant role through start-up and ongoing support to ensure that digital games and simulations will not only excite and entertain, but also motivate and educate.
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Python Machine Learning for Beginners

This book takes you on a practical journey through the fantastic features of Python. Unlike books that focus on theoretical concepts only, this book will show you how Python is used - and encourage you to get creative!

Author: Andrew Johnson

Publisher: Amplitudo Limited

ISBN: 1801140154

Category:

Page: 362

View: 762

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Would you like to start programming with Python from scratch? This is the easiest way you can find it! What are you waiting for? Keep reading! The PROGRAMMING LANGUAGES ACADEMY has created a targeted learning path within reach of anyone who wants to start programming without appropriate skills. In this book, you will find a real step by step path that will take you from 0 to 100 in a few days!!! Once you start reading, you will appreciate a simple, straightforward, and essential guide. The chapters are short and will deliver new information slowly to avoid being overwhelmed by too many notions altogether. Illustrations, examples, and step-by-step guides in each chapter allow you not to make mistakes but, above all, not to confuse. You no longer have to waste time and money trying to learn Python from expensive online courses or from incredibly long textbooks that leave you just more confused and frustrated. Python Workbook: Learn How to Quickly and Effectively Program with Exercises, Projects, and Solutions Do you want to learn one of today's most in-demand programming languages and start an exciting career in data science, web development, or another field of your choice? Learn Python! Python is easy to read because the code looks a lot like regular English, but don't let this simplicity deceive you: it's one of the most influential and versatile programming languages out there! It powers many of your favorite websites and services, including Instagram, Spotify, and even Google! This book takes you on a practical journey through the fantastic features of Python. Unlike books that focus on theoretical concepts only, this book will show you how Python is used - and encourage you to get creative! Here's what you'll find in this book: Practical programming exercises that will help you apply programming concepts to real-life situations Debugging activities that will teach you to notice errors in Python code quickly Fun projects that will test your knowledge and motivate you to practice even more Valuable tips for mastering Python quickly An answer key to check if you were right Learning the basics of any programming language may seem a bit boring at first, but once you've written your first program that does something - even if it's just printing text on the screen - your excitement and motivation will become unstoppable. You'll yearn for more and more programming challenges that will hone your skills! If you've tried learning Python before but got discouraged by too much theory... this book is guaranteed to rekindle your interest in Python programming! Are you ready to start writing Python apps that work? If you're prepared to learn the basics of python programming 7 DAYS FROM TODAY, get a copy of this book today!
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Hands On Data Science for Marketing

This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.

Author: Yoon Hyup Hwang

Publisher: Packt Publishing Ltd

ISBN: 9781789348828

Category: Computers

Page: 464

View: 389

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Optimize your marketing strategies through analytics and machine learning Key Features Understand how data science drives successful marketing campaigns Use machine learning for better customer engagement, retention, and product recommendations Extract insights from your data to optimize marketing strategies and increase profitability Book Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learn Learn how to compute and visualize marketing KPIs in Python and R Master what drives successful marketing campaigns with data science Use machine learning to predict customer engagement and lifetime value Make product recommendations that customers are most likely to buy Learn how to use A/B testing for better marketing decision making Implement machine learning to understand different customer segments Who this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.
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Ensemble Machine Learning

Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, ...

Author: Cha Zhang

Publisher: Springer Science & Business Media

ISBN: 9781441993250

Category: Computers

Page: 332

View: 897

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It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.
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