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Advanced Decision Sciences Based On Deep Learning And Ensemble Learning Algorithms
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Book Synopsis Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms by : S. Sumathi
Download or read book Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms written by S. Sumathi and published by Nova Science Publishers. This book was released on 2021 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python describes the deep learning models and ensemble approaches applied to decision-making problems. The authors have addressed the concepts of deep learning, convolutional neural networks, recurrent neural networks, and ensemble learning in a practical sense providing complete code and implementation for several real-world examples. The authors of this book teach the concepts of machine learning for undergraduate and graduate-level classes and have worked with Fortune 500 clients to formulate data analytics strategies and operationalize these strategies. The book will benefit information professionals, programmers, consultants, professors, students, and industry experts who seek a variety of real-world illustrations with an implementation based on machine learning algorithms"--
Book Synopsis Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms by : S. Sumathi
Download or read book Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms written by S. Sumathi and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python describes the deep learning models and ensemble approaches applied to decision-making problems. The authors have addressed the concepts of deep learning, convolutional neural networks, recurrent neural networks, and ensemble learning in a practical sense providing complete code and implementation for several real-world examples. The authors of this book teach the concepts of machine learning for undergraduate and graduate-level classes and have worked with Fortune 500 clients to formulate data analytics strategies and operationalize these strategies. The book will benefit information professionals, programmers, consultants, professors, students, and industry experts who seek a variety of real-world illustrations with an implementation based on machine learning algorithms"--
Book Synopsis Machine Learning for Decision Sciences with Case Studies in Python by : S. Sumathi
Download or read book Machine Learning for Decision Sciences with Case Studies in Python written by S. Sumathi and published by CRC Press. This book was released on 2022-07-06 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning. This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.
Book Synopsis Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare by : Ilker Ozsahin
Download or read book Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare written by Ilker Ozsahin and published by Bentham Science Publishers. This book was released on 2021-11-18 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an ideal foundation for readers to understand the application of artificial intelligence (AI) and machine learning (ML) techniques to expert systems in the healthcare sector. It starts with an introduction to the topic and presents chapters which progressively explain decision-making theory that helps solve problems which have multiple criteria that can affect the outcome of a decision. Key aspects of the subject such as machine learning in healthcare, prediction techniques, mathematical models and classification of healthcare problems are included along with chapters which delve in to advanced topics on data science (deep-learning, artificial neural networks, etc.) and practical examples (influenza epidemiology and retinoblastoma treatment analysis). Key Features: - Introduces readers to the basics of AI and ML in expert systems for healthcare - Focuses on a problem solving approach to the topic - Provides information on relevant decision-making theory and data science used in the healthcare industry - Includes practical applications of AI and ML for advanced readers - Includes bibliographic references for further reading The reference is an accessible source of knowledge on multi-criteria decision-support systems in healthcare for medical consultants, healthcare policy makers, researchers in the field of medical biotechnology, oncology and pharmaceutical research and development.
Book Synopsis Artificial Intelligence and Deep Learning for Decision Makers by : Kaur Dr. Jagreet
Download or read book Artificial Intelligence and Deep Learning for Decision Makers written by Kaur Dr. Jagreet and published by BPB Publications. This book was released on 2019-12-28 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn modern-day technologies from modern-day technical giants.KEY FEATURES1. Real-world success and failure stories of artificial intelligence explained2. Understand concepts of artificial intelligence and deep learning methods 3. Learn how to use artificial intelligence and deep learning methods4. Know how to prepare dataset and implement models using industry leading Python packages 5. You'll be able to apply and analyze the results produced by the models for predictionDESCRIPTION The aim of this book is to help the readers understand the concept of artificial intelligence and deep learning methods and implement them into their businesses and organizations. The first two chapters describe the introduction of the artificial intelligence and deep learning methods. In the first chapter, the concept of human thinking process, starting from the biochemical responses within the structure of neurons to the problem-solving steps through computational thinking skills are discussed. All chapters after the first two should be considered as the study of different technological and Artificial Intelligence giants of current age. These chapters are placed in a way that each chapter could be considered a separate study of a separate company, which includes the achievements of intelligent services currently provided by the company, discussion on the business model of the company towards the use of the deep learning technologies, the advancement of the web services which are incorporated with intelligent capability introduced by company, the efforts of the company in contributing to the development of the artificial intelligence and deep learning research. WHAT WILL YOU LEARN How to use the algorithms written in the Python programming language to design models and perform predictions in general datasetsUnderstand use cases in different industries related to the implementation of artificial intelligence and deep learning methodsLearn the use of potential ideas in artificial intelligence and deep learning methods to improve the operational processes or new products and how services can be produced based on the methodsWHO THIS BOOK IS FORThis book is targeted to business and organization leaders, technology enthusiasts, professionals, and managers who seek knowledge of artificial intelligence and deep learning methods.Table of Contents1. Artificial Intelligence and Deep Learning2. Data Science for Business Analysis3. Decision Making4. Intelligent Computing Strategies By Google 5. Cognitive Learning Services in IBM Watson6. Advancement web services by Baidu 7. Improved Social Business by Facebook8. Personalized Intelligent Computing by Apple9. Cloud Computing Intelligent by MicrosoftAbout the AuthorDr. Jagreet KaurDr. Jagreet Kaur is a doctorate in computer science and engineering. Her topic of thesis was "e;ARTIFICIAL INTELLIGENCE BASED ANALYTICAL PLATFORM FOR PREDICTIVE ANALYSIS IN HEALTH CARE."e; With more than 12 years of experience in academics and research, she is working in data wrangling, machine learning and deeplearning algorithms on large datasets, real-time data often in production environments for data science solutions and data products to get actionable insights for the last four years. She also possesses ten international publications and five national publications under her name.Her skill set includes data engineering skills (Hadoop, Apache Spark, Apache Kafka, Cassandra, Hive, Flume, Scoop, and Elasticsearch), programming skills (Python, Angularjs, D3.js , Machine Learning, and R), data science skills (Statistics, Machine Learning, NLP, NLTK, Artificial Intelligence, R, Python, Pandas, Sklearn, Hadoop, SQL, Statistical Modeling, Data Munging, Decision Science, Machine Learning, Graph Analysis, Text Mining and Optimization, and Web Scraping, Deep learning packages:- Theano, Keras, Tensorflow, Pytorch, Julia) and Algorithms Specialization (Regression Algorithms: Linear Regression, Random Forest Regressor, XGBoost, SVR, Ridge Regression, Lasso Regression, Neural Networks Classification Algorithms: Decision Trees, Random Forest Classifier, Support Vector Machines(SVM), Logistic Regression, KNN Classifier, Neural Network, Clustering Algorithms: K-Means, DBSCAN, Deep Learning Algorithms: Simple RNN, LSTM Network, GRU)Currently, she works as a Chief Operating Officer (COO) and Chief Data Scientist in Xenonstack. Under her Guidance, more than 400 projects are already developed and productionized which also includes more than 200 AI and data science projects. Navdeep Singh GillNaveed Singh Gill is a technology and solution architect having more than 15 years of experience in the IT and Telecom industry. For the past six years, he is working in big data analytics, automation and advanced analytics using machine learning and deep learning for planning and architecting of data science solutions and data products. He's also working in 3 As (Analytics, Automation, and AI), more focused on writing software for building data lake, analytics platform , NoSQL deployments, data migration, data modelling tasks, ML/DL on real-time data often in production environments.He started his career with HFCL Infotel as a network engineer for managing the technical network of Broadband Customers with Linux servers and Cisco routers. He also worked in Ericsson, where he handled the synchronization plan and implementation for synchronization of Microwave Network and Media Gateway, MSS, and Core Network. SSU Implementation Planning and Optimization with respect to IP RAN, Mobile Backhaul Solution- Optimization of Existing Microwave Network to Ethernet, Microwave Hybrid Solution, Convergence to all IP, SIU Implementation for conversion to IP of Existing BTS,GB over IP.His area of expertise includes Hadoop, Openstack, DevOps, Kubernetes, Dockers, Amazon web services, Apache Spark, Apache Storm, Apache Kafka, Hbase, Solr, Apache FlinkNutch, Mapreduce, Pig, Hive, Flume, Scoop, ElasticSearch, and programming expertise includes Python, Angular.js, and Node.js.
Book Synopsis Machine Learning for Intelligent Decision Science by : Jitendra Kumar Rout
Download or read book Machine Learning for Intelligent Decision Science written by Jitendra Kumar Rout and published by Springer Nature. This book was released on 2020-04-02 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.
Download or read book Machine Learning written by and published by BoD – Books on Demand. This book was released on 2021-12-22 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses–cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.
Book Synopsis Ensemble Machine Learning by : Cha Zhang
Download or read book Ensemble Machine Learning written by Cha Zhang and published by Springer Science & Business Media. This book was released on 2012-02-17 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis A Greater Foundation for Machine Learning Engineering by : Dr. Ganapathi Pulipaka
Download or read book A Greater Foundation for Machine Learning Engineering written by Dr. Ganapathi Pulipaka and published by Xlibris Corporation. This book was released on 2021-10-01 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning. The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods. The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms. This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.
Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Book Synopsis Hands-On Ensemble Learning with Python by : George Kyriakides
Download or read book Hands-On Ensemble Learning with Python written by George Kyriakides and published by Packt Publishing Ltd. This book was released on 2019-07-19 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combine popular machine learning techniques to create ensemble models using Python Key FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook Description Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you'll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios. What you will learnImplement ensemble methods to generate models with high accuracyOvercome challenges such as bias and varianceExplore machine learning algorithms to evaluate model performanceUnderstand how to construct, evaluate, and apply ensemble modelsAnalyze tweets in real time using Twitter's streaming APIUse Keras to build an ensemble of neural networks for the MovieLens datasetWho this book is for This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book.
Book Synopsis Machine Learning and Its Applications by : Georgios Paliouras
Download or read book Machine Learning and Its Applications written by Georgios Paliouras and published by Springer. This book was released on 2003-06-29 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.
Book Synopsis Applied Deep Learning by : Umberto Michelucci
Download or read book Applied Deep Learning written by Umberto Michelucci and published by Apress. This book was released on 2018-09-07 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). What You Will Learn Implement advanced techniques in the right way in Python and TensorFlow Debug and optimize advanced methods (such as dropout and regularization) Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) Set up a machine learning project focused on deep learning on a complex dataset Who This Book Is For Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.
Book Synopsis Fundamentals and Methods of Machine and Deep Learning by : Pradeep Singh
Download or read book Fundamentals and Methods of Machine and Deep Learning written by Pradeep Singh and published by John Wiley & Sons. This book was released on 2022-03-02 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.
Book Synopsis Ensemble Machine Learning Cookbook by : Dipayan Sarkar
Download or read book Ensemble Machine Learning Cookbook written by Dipayan Sarkar and published by Packt Publishing Ltd. This book was released on 2019-01-31 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more Key FeaturesApply popular machine learning algorithms using a recipe-based approachImplement boosting, bagging, and stacking ensemble methods to improve machine learning modelsDiscover real-world ensemble applications and encounter complex challenges in Kaggle competitionsBook Description Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis. By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes. What you will learnUnderstand how to use machine learning algorithms for regression and classification problemsImplement ensemble techniques such as averaging, weighted averaging, and max-votingGet to grips with advanced ensemble methods, such as bootstrapping, bagging, and stackingUse Random Forest for tasks such as classification and regressionImplement an ensemble of homogeneous and heterogeneous machine learning algorithmsLearn and implement various boosting techniques, such as AdaBoost, Gradient Boosting Machine, and XGBoostWho this book is for This book is designed for data scientists, machine learning developers, and deep learning enthusiasts who want to delve into machine learning algorithms to build powerful ensemble models. Working knowledge of Python programming and basic statistics is a must to help you grasp the concepts in the book.
Book Synopsis Practical Machine Learning by : Sunila Gollapudi
Download or read book Practical Machine Learning written by Sunila Gollapudi and published by Packt Publishing Ltd. This book was released on 2016-01-30 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Ensemble Learning Algorithms With Python by : Jason Brownlee
Download or read book Ensemble Learning Algorithms With Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2021-04-26 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively improve predictive modeling performance using ensemble algorithms.
