Practical Machine Learning in R

Author :
Publisher : John Wiley & Sons
Release Date :
ISBN 10 : 9781119591511
Pages : 464 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (591 users download)


Download Practical Machine Learning in R by Fred Nwanganga PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Machine Learning in R by Fred Nwanganga. This book is available in ePub and PDF format with a single click unlimited downloads. Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.


Practical Machine Learning with R

Author :
Publisher : Packt Publishing Ltd
Release Date :
ISBN 10 : 9781838552848
Pages : 416 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.5/5 (552 users download)


Download Practical Machine Learning with R by Brindha Priyadarshini Jeyaraman PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Machine Learning with R by Brindha Priyadarshini Jeyaraman. This book is available in ePub and PDF format with a single click unlimited downloads. Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems Key Features Gain a comprehensive overview of different machine learning techniques Explore various methods for selecting a particular algorithm Implement a machine learning project from problem definition through to the final model Book Description With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it. What you will learn Define a problem that can be solved by training a machine learning model Obtain, verify and clean data before transforming it into the correct format for use Perform exploratory analysis and extract features from data Build models for neural net, linear and non-linear regression, classification, and clustering Evaluate the performance of a model with the right metrics Implement a classification problem using the neural net package Employ a decision tree using the random forest library Who this book is for If you are a data analyst, data scientist, or a business analyst who wants to understand the process of machine learning and apply it to a real dataset using R, this book is just what you need. Data scientists who use Python and want to implement their machine learning solutions using R will also find this book very useful. The book will also enable novice programmers to start their journey in data science. Basic knowledge of any programming language is all you need to get started.


Data Mining

Author :
Publisher : Morgan Kaufmann
Release Date :
ISBN 10 : 9780128043578
Pages : 654 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.4/5 (43 users download)


Download Data Mining by Ian H. Witten PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Mining by Ian H. Witten. This book is available in ePub and PDF format with a single click unlimited downloads. Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book


Practical Machine Learning

Author :
Publisher : Packt Publishing Ltd
Release Date :
ISBN 10 : 9781784394011
Pages : 468 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (394 users download)


Download Practical Machine Learning by Sunila Gollapudi PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Machine Learning by Sunila Gollapudi. This book is available in ePub and PDF format with a single click unlimited downloads. Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn Implement a wide range of algorithms and techniques for tackling complex data Get to grips with some of the most powerful languages in data science, including R, Python, and Julia Harness the capabilities of Spark and Hadoop to manage and process data successfully Apply the appropriate machine learning technique to address real-world problems Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data. This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naive Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies. Style and approach A practical data science tutorial designed to give you an insight into the practical application of machine learning, this book takes you through complex concepts and tasks in an accessible way. Featuring information on a wide range of data science techniques, Practical Machine Learning is a comprehensive data science resource.


Practical Machine Learning for Data Analysis Using Python

Author :
Publisher : Academic Press
Release Date :
ISBN 10 : 9780128213803
Pages : 534 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.1/5 (213 users download)


Download Practical Machine Learning for Data Analysis Using Python by Abdulhamit Subasi PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Machine Learning for Data Analysis Using Python by Abdulhamit Subasi. This book is available in ePub and PDF format with a single click unlimited downloads. Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features


Practical Machine Learning: A New Look at Anomaly Detection

Author :
Publisher : "O'Reilly Media, Inc."
Release Date :
ISBN 10 : 9781491914182
Pages : 66 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.1/5 (914 users download)


Download Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning. This book is available in ePub and PDF format with a single click unlimited downloads. Finding Data Anomalies You Didn't Know to Look For Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what “suspects” you’re looking for. This O’Reilly report uses practical examples to explain how the underlying concepts of anomaly detection work. From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The concepts described in this report will help you tackle anomaly detection in your own project. Use probabilistic models to predict what’s normal and contrast that to what you observe Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model Use historical data to discover anomalies in sporadic event streams, such as web traffic Learn how to use deviations in expected behavior to trigger fraud alerts


Practical Machine Learning with H2O

Author :
Publisher : "O'Reilly Media, Inc."
Release Date :
ISBN 10 : 9781491964576
Pages : 300 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.6/5 (964 users download)


Download Practical Machine Learning with H2O by Darren Cook PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Machine Learning with H2O by Darren Cook. This book is available in ePub and PDF format with a single click unlimited downloads. Learn how to construct machine learning and data analysis scalable for big data using H2O software, using sample data sets and several machine-learning techniques including deep learning, random forests, unsupervised learning and ensemble learning.


Practical Machine Learning with R and Python: Third Edition

Author :
Publisher :
Release Date :
ISBN 10 : 1792969309
Pages : 276 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (792 users download)


Download Practical Machine Learning with R and Python: Third Edition by Tinniam V. Ganesh PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Machine Learning with R and Python: Third Edition by Tinniam V. Ganesh. This book is available in ePub and PDF format with a single click unlimited downloads. This is the 3rd edition of the book. All the code sections are formatted with fixed-width font Consolas for better readability. This book implements many common Machine Learning algorithms in equivalent R and Python. The book touches on R and Python implementations of different regression models, classification algorithms including logistic regression, KNN classification, SVMs, b-splines, random forest, boosting etc. Other techniques like best-fit, forward fit, backward fit, and lasso and ridge regression are also covered. The book further touches on classification metrics for computing accuracy, recall, precision etc. There are implementations of validation, ROC and AUC curves in both R and Python. Finally, the book covers unsupervised learning methods like K-Means, PCA and Hierarchical clustering.The book is well suited for the novice and the expert. The first two chapters discuss the most important programming constructs in R and Python. The third chapter highlights equivalent programming phrases in R and Python. Hence, those with no knowledge of R and Python will find these introductory chapters useful. Those who are proficient in one of the language can further their knowledge on the other. Those are familiar with both R and Python will find the equivalent implementations useful to internalize the algorithms. This book should serve as a useful and handy reference for Machine Learning algorithms in both R and Python


Data Mining: Practical Machine Learning Tools and Techniques

Author :
Publisher : Elsevier
Release Date :
ISBN 10 : 9780080890364
Pages : 664 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (89 users download)


Download Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten. This book is available in ePub and PDF format with a single click unlimited downloads. Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization


Practical Machine Learning with Python

Author :
Publisher : Apress
Release Date :
ISBN 10 : 9781484232071
Pages : 530 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.3/5 (232 users download)


Download Practical Machine Learning with Python by Dipanjan Sarkar PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Machine Learning with Python by Dipanjan Sarkar. This book is available in ePub and PDF format with a single click unlimited downloads. Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students


Practical Machine Learning with R and Python: Second Edition

Author :
Publisher :
Release Date :
ISBN 10 : 1983035661
Pages : 269 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.8/5 (983 users download)


Download Practical Machine Learning with R and Python: Second Edition by Tinniam V. Ganesh PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Machine Learning with R and Python: Second Edition by Tinniam V. Ganesh. This book is available in ePub and PDF format with a single click unlimited downloads. This is the 2nd edition of the book. This 2nd edition includes more content, detailed code comments and better formatting for readbility. This book implements many common Machine Learning algorithms in equivalent R and Python. The book touches on R and Python implementations of different regression models, classification algorithms including logistic regression, KNN classification, SVMs, b-splines, random forest, boosting etc. Other techniques like best-fit, forward fit, backward fit, and lasso and ridge regression are also covered. The book further touches on classification metrics for computing accuracy, recall, precision etc. There are implementations of validation, ROC and AUC curves in both R and Python. Finally, the book covers unsupervised learning methods like K-Means, PCA and Hierarchical clustering.The book is well suited for the novice and the expert. The first two chapters discuss the most important programming constructs in R and Python. The third chapter highlights equivalent programming phrases in R and Python. Hence, those with no knowledge of R and Python will find these introductory chapters useful. Those who are proficient in one of the language can further their knowledge on the other. Those are familiar with both R and Python will find the equivalent implementations useful to internalize the algorithms. This book should serve as a useful and handy reference for Machine Learning algorithms in both R and Python


Data Mining

Author :
Publisher : Morgan Kaufmann
Release Date :
ISBN 10 : UOM:39015056688602
Pages : 371 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4./5 ( users download)


Download Data Mining by Ian H. Witten PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Mining by Ian H. Witten. This book is available in ePub and PDF format with a single click unlimited downloads. This book offers a thorough grounding in machine learning concepts combined with practical advice on applying machine learning tools and techniques in real-world data mining situations. Clearly written and effectively illustrated, this book is ideal for anyone involved at any level in the work of extracting usable knowledge from large collections of data. Complementing the book's instruction is fully functional machine learning software.


Practical Machine Learning with R and Python

Author :
Publisher :
Release Date :
ISBN 10 : 1973443503
Pages : 244 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.7/5 (973 users download)


Download Practical Machine Learning with R and Python by Tinniam V. Ganesh PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Machine Learning with R and Python by Tinniam V. Ganesh. This book is available in ePub and PDF format with a single click unlimited downloads. This book implements many common Machine Learning algorithms in equivalent R and Python. The book touches on R and Python implementations of different regression models, classification algorithms including logistic regression, KNN classification, SVMs, b-splines, random forest, boosting etc. Other techniques like best-fit, forward fit, backward fit, and lasso and ridge regression are also covered. The book further touches on classification metrics for computing accuracy, recall, precision etc. There are implementations of validation, ROC and AUC curves in both R and Python. Finally, the book covers unsupervised learning methods like K-Means, PCA and Hierarchical clustering.The book is well suited for the novice and the expert. The first two chapters discuss the most important programming constructs in R and Python. The third chapter highlights equivalent programming phrases in R and Python. Hence, those with no knowledge of R and Python will find these introductory chapters useful. Those who are proficient in one of the language can further their knowledge on the other. Those are familiar with both R and Python will find the equivalent implementations useful to internalize the algorithms. This book should serve as a useful and handy reference for Machine Learning algorithms in both R and Python


Practical Machine Learning for Computer Vision

Author :
Publisher : "O'Reilly Media, Inc."
Release Date :
ISBN 10 : 9781098102333
Pages : 482 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.0/5 (12 users download)


Download Practical Machine Learning for Computer Vision by Valliappa Lakshmanan PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Machine Learning for Computer Vision by Valliappa Lakshmanan. This book is available in ePub and PDF format with a single click unlimited downloads. This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models


Machine Learning For Dummies

Author :
Publisher : John Wiley & Sons
Release Date :
ISBN 10 : 9781119245513
Pages : 432 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.4/5 (245 users download)


Download Machine Learning For Dummies by John Paul Mueller PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning For Dummies by John Paul Mueller. This book is available in ePub and PDF format with a single click unlimited downloads. Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!


Machine Learning

Author :
Publisher : Springer
Release Date :
ISBN 10 : 9783319949895
Pages : 362 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.4/5 (949 users download)


Download Machine Learning by RODRIGO F MELLO PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning by RODRIGO F MELLO. This book is available in ePub and PDF format with a single click unlimited downloads. This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.


Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies

Author :
Publisher : IGI Global
Release Date :
ISBN 10 : 9781799844457
Pages : 324 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.4/5 (844 users download)


Download Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies by Sarfraz, Muhammad PDF/Ebook Free clicking on the below button will initiate the downloading process of Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies by Sarfraz, Muhammad. This book is available in ePub and PDF format with a single click unlimited downloads. Two significant areas of study that are continually impacting various dimensions in computer science are computer vision and imaging. These technologies are rapidly enhancing how information and data is being exchanged and opening numerous avenues of advancement within areas such as multimedia and intelligent systems. The high level of applicability in computer vision and image processing requires significant research on the specific utilizations of these technologies. Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies is an essential reference source that discusses innovative developments in computational imaging for solving real-life issues and problems and addresses their execution in various disciplines. Featuring research on topics such as image modeling, remote sensing, and support vector machines, this book is ideally designed for IT specialists, scientists, researchers, engineers, developers, practitioners, industry professionals, academicians, and students seeking coverage on the latest developments and innovations in computer vision applications within the realm of multimedia systems.


Popular Ebook