44 Best Supervised Learning Books of All Time
As featured on CNN, Forbes and Inc – BookAuthority identifies and rates the best books in the world. Learn more
1
Key Features
Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics
Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering
Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python
...moreLearn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics
Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering
Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python
Book DescriptionComplex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and moreBy the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problemWhat you will learnUnderstand the Statistical and Machine Learning fundamentals necessary to build modelsUnderstand the major differences and parallels between the statistical way and the Machine Learning way to solve problemsLearn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packagesAnalyze the results and tune the model appropriately to your own predictive goalsUnderstand the concepts of required statistics for
()2
Teach your machine to think for itself!
Key Features
Delve into supervised learning and grasp how a machine learns from data
Implement popular machine learning algorithms from scratch, developing a deep understanding along the way
...moreKey Features
Delve into supervised learning and grasp how a machine learns from data
Implement popular machine learning algorithms from scratch, developing a deep understanding along the way
Explore some of the most popular scientific and mathematical libraries in the Python language Book Description Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You'll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you'll wrap up with a brief foray into neural networks and transfer learning By the end of this book, you'll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems What you will learn Crack how a machine learns a concept and generalize its understanding to new data Uncover the fundamental differences between parametric and non-parametric models Implement and grok several well-known supervised learning algorithms from scratch Work with models in domains such as ecommerce and marketing Expand your expertise and use various algorithms such as regression, decision trees, and clustering Build your own models capable of making predictions Delve into the most popular approaches in deep learning such as transfer learning and neural networks Who this book is for This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming―and some fundamental knowledge of supervised learning―are expectedTable of Contents First step towards supervised learning Implementing parametric models Working with non-parametric models Advanced topics in supervised machine learning
()See also:Machine Learning
3
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning.
...moreThe many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates
()See also:Data Mining, Neural Networks
4
****Buy now (Will soon return to $35.99 + Special Offer Below) ****
Free Kindle eBook for customers who purchase the print book
Are you thinking of learning more about Machine Learning with Practical Examples using Python?
Machine learning is a field of Artificial Intelligence that uses algorithms to learn from data and make predictions. This means that we can feed data into an algorithm, and use it to make predictions about what might happen in the future. If you are looking for a book to help you understand how the Machine learning works by using Python, then this is a good book for you.
...moreFree Kindle eBook for customers who purchase the print book
Are you thinking of learning more about Machine Learning with Practical Examples using Python?
Machine learning is a field of Artificial Intelligence that uses algorithms to learn from data and make predictions. This means that we can feed data into an algorithm, and use it to make predictions about what might happen in the future. If you are looking for a book to help you understand how the Machine learning works by using Python, then this is a good book for you.
Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images which detail all algorithms and their applications in all area of the real life. Why this book is different? This book takes a different approach that is based on providing simple examples of how machine learning algorithms work, and building on those examples step by step to encompass the more complicated parts of the algorithms The book is a practical guide through the basic principles of machine learning, and how to get started with machine learning using Python based on libraries that make it easy to start. Python Codes for the Examples Shown In the Book You will build your machine learning model by using PythonTarget Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach machine learning practices, but are too afraid to startNewbies in computer science techniques and machine learningProfessionals in data science and social sciencesProfessors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest wayStudents and academicians, especially those focusing on machine learning and deep learningWhat’s Inside this Book? Introduction to Machine Learning? Essential Libraries and their Installation Basic of Python Language in Machine Learning Data and Inconsistencies in Machine Learning A Roadmap for building Machine Learning Systems Data Cleaning and Preparation Application of Supervised Learning Techniques with Python Applications of unsupervised learning Techniques with python Training Machine Learning Algorithms Combining Different Models for ensemble learning Frequently Asked QuestionsQ: Is this book for me and do I need programming experience? A: If you want to smash machine learning problems with Python and TensorFlow, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you’ll be OK. Q: Can I loan this book to friends? A: Yes. Under Amazon’s Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and further learning will be required beyond this book to master all aspects of machine learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. will also be happy to help you if you send us an email at customer_service@datasciences-book.com.
()See also:Machine Learning, Machine Learning Model
5
Do you know how to build a Machine Learning Algorithm in Python?
Have you learned how to build a Neural Network in Python?
If you have read the first three books in the series, you will know how to do both those things. If you want to learn more about the concepts related to Machine Learning, and some subjects and concepts that are linked to Machine Learning, you have come to the right place
Over the course of the book, you will gather information on the following:
...moreHave you learned how to build a Neural Network in Python?
If you have read the first three books in the series, you will know how to do both those things. If you want to learn more about the concepts related to Machine Learning, and some subjects and concepts that are linked to Machine Learning, you have come to the right place
Over the course of the book, you will gather information on the following:
Subjects linked to Machine Learning Artificial Intelligence Big Data Building Generic Algorithms in Python Activation functions used to build Neural Networks Building a Neural Network in R The information in this book will help you learn more about Machine Learning. You should now be able to link some of the concepts in Machine Learning with the work you do, or the work you want to do. Once you practice the models in the book, you can build your very own models in either R or PythonSo What are You Waiting For? It is never to early or late to learn. Grab a copy of this book now, and build your very own genetic Algorithm in Python and a Neural Network in R
()See also:Machine Learning, Learning Algorithms
6
Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students.
...more ()
See also:Particle Physics
7
Do you understand the difference between supervised and unsupervised learning algorithms? Would you like to be able to learn about this quickly, in a simple and concise way? Some of the world’s biggest companies use machine learning techniques to manage their functions in an effective and professional way that is beneficial to their business.
...moreNow you can understand more about them with Machine Learning for Absolute Beginners, a book which provides you with in-depth knowledge of the Supervised and Unsupervised Learning Algorithms which play such a big part and specifically: Supervised learning Semi-supervised learning Learning to learn Transduction Learning that reinforces Written in a clear and easy-to-follow style, this book takes what might seem to be a complex subject and simplifies it in basic layman’s terms, so that it becomes an easy read, even if you are a complete novice. Get yourself a copy of Machine Learning for Absolute Beginners today. It will enhance your understanding and provide you with everything you’ll need to expand your knowledge of this fascinating subject!
()See also:Machine Learning
8
Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition)
...moreThis book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research
()See also:Neural Networks, Pattern Recognition
9
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data is unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data is labeled.The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination.
...moreSemi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data is scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field.
()See also:Machine Learning, Semi Supervised Learning
10
Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python
BONUS Buy a paperback copy of this book NOW and you will get the Kindle version Absolutely FREE via Kindle Matchbook
Do you want to MASTER Data science?
Understand Markov Models and learn the real world application to accurately predict future events
...moreBONUS Buy a paperback copy of this book NOW and you will get the Kindle version Absolutely FREE via Kindle Matchbook
Do you want to MASTER Data science?
Understand Markov Models and learn the real world application to accurately predict future events
Extend your knowledge of machine learning, python programming & algorithms What you'll Learn Mathematics Behind Markov Algorithms 3 Main Problems Of Markov Models And How To Overcome Them Uses And Applications For Machine Learning Python Programming Speech Recognition Weather Reporting The Markov Rule And Markov’s Model Fundamental Axioms Of Statistics And Probability Solutions Theories Artificial Intelligence Bayesian Inference Important Tools Used With HMM And Much, Much, More! The objective of this book is to teach you the essentials at the most fundamental level You will learn the ins and outs of machine learning, and its real world applications Also, specifically you will discover practical implementations of Markov Models in python programming This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run Why not take this opportunity to take advantage now and get ahead of everyone else? Other books can easily retail for $100s- $1000s of dollars! Get equipped with the knowledge you need to advance yourself today at an affordable price What are you waiting for? Don't miss out on this opportunity! Grab Your Copy Now!
()See also:Machine Learning
11
**BONUS** Buy a paperback copy of this book NOW and you will get the Kindle version Absolutely FREE via Kindle Matchbook Machines can LEARN ?!?!
Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures
Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning
Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead? Its a worthwhile read and the most valuable investment you can make for yourself What You'll Learn
...moreMachine learning occurs primarily through the use of " algorithms" and other elaborate procedures
Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning
Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead? Its a worthwhile read and the most valuable investment you can make for yourself What You'll Learn
Supervised Learning Unsupervised Learning Reinforced Learning Algorithms Decision Tree Random Forest Neural Networks Python Deep Learning And much, much more! This is the most comprehensive and easy to read step by step guide in machine learning that exists. Learn from one of the most reliable programmers alive and expert in the field Grab your copy now! You do not want to miss out on this incredible offer! Read this book for FREE on Kindle Unlimited
()See also:Decision Tree, Machine Learning
12
This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available.
...moreThe author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topicsAllows readers to analyze data sets with small samples and many features;Provides a fast algorithm, based upon linear algebra, to analyze big data;Includes several applications to multi-view data analyses, with a focus on bioinformatics
()See also:Feature Extraction
13
Computers can't LEARN... Right?!
Machine Learning is a branch of computer science that wants to stop programming computers using a list of detailed instructions and instead use a set of high-level commands which they can apply to many unknown scenarios – these are called algorithms
In practice, they want to give computers the ability to Learn and to ADAPT
We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before (or even knew existed).
...moreMachine Learning is a branch of computer science that wants to stop programming computers using a list of detailed instructions and instead use a set of high-level commands which they can apply to many unknown scenarios – these are called algorithms
In practice, they want to give computers the ability to Learn and to ADAPT
We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before (or even knew existed).
Unfortunately, the true power and applications of today’s Machine Learning Algorithms is misunderstood by most people Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry: Supervised Learning Algorithms K-Nearest Neighbour Naïve BayesRegressionsUnsupervised Learning Algorithms: Support Vector Machines Decision Trees
()See also:Machine Learning
14
Alzheimer's Disease is the most common type of dementia disease affecting millions of people across the globe. It involves degeneration of the brain which is irreversible and gradually ends up with the complete brain failure. This book presents a supervised learning model to effectively capture the complex feature interactions, in the sample space of Alzheimer's Disease data, for knowledge discovery. The discovered knowledge is further used to quantify the similarity of a test subject to the demented class.
...moreThe outcome of the work clearly demonstrates that, supervised learning model can be used effectively to quantify the severity of of Alzheimer's Disease on a standard measurable scale. This scale of distance can be used as a supplement for Clinical Dementia Rating.
()See also:Alzheimer's
15
Text categorization has become the core research area in text mining domain. Semi-supervised learning paradigm is relatively new learning paradigm compared to supervised and unsupervised learning, semi-supervised learning paradigm is introduced. The topics in the book are introduced such that a novice reader can grasp them easily. The simple to implement, traditional K-Nearest Neighbour learning algorithm is employed in the process of categorization. Terms importance with respect to each class label are identified and are used to improve the performance of the traditional KNN algorithm.
...more ()
See also:Semi Supervised Learning
16
Network Function Virtualization (NFV) is an emerging solution that improves the flexibility, efficiency, and manageability of networks by leveraging virtualization and cloud computing technologies to run networked devices in software. The implementation of NFV presents issues such as the introduction of new software components, bottleneck performance and monitoring of hidden traffic. A considerable amount of NFV traffic is invisible using traditional monitoring strategies because it does not hit a physical link.
...moreThe implementation of autonomous management and supervised algorithms of Machine Learning (ML) become a key strategy to manage this hidden traffic. In this research, we focus on analyzing NFV traffic features in two test environments with different components and traffic generation. We perform a benchmarking of the performance of supervised ML algorithms concerning its efficiency; considering that the efficiency of the algorithms depends on the trade-off between the time-response and the precision achieved in the classication. The results show that the NaiveBayes and C4.5 algorithms reach values greater than 90.68 % in a response time range between 0.37 sec and 3 sec.
()17
Data mining tools are best approach for Criminal identification based on characteristic and nature of crime. In this book, we have proposed a supervised approach for identifying the suspected list of criminal’s using similarity measure and K-Medoids cluster algorithm. K-Medoids clustering algorithm groups the more closely related crimes as an individual group and each group will have unique set of features. The unique features set is used for identification of criminal using similarity measure algorithms based on distance measure. The proposed system has two phase, training and testing phase.
...moreIn this approach, we have trained the proposed system with supervised data set with collected crime information from various places of Tamil Nadu through online available data. In the testing phase, first identify the cluster closest to the test crime by using K-Medoids clustering algorithm and then identify the suspected criminal list using similarity measure. The initial stage of implementation and analysis of the proposed scheme provides good results and high accuracy. The proposed scheme is compared with related K-Means clustering algorithm with same set of training and test instances.
()18
This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics.
...moreIt shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learningIncludes advances on unsupervised learning using natural computing techniquesReports on topics in emerging areas such as evolutionary multi-objective unsupervised learningFeatures natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms
()19
This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of growth of data vastly outpacing any chance of labeling them manually. The book looks at the underlying algorithms and technologies that facilitate the area within big data analytics, it covers their applications across domains such as smarter transportation, social media, fake news detection and enterprise search among others.
...moreThis book enables readers to understand a spectrum of advances in this emerging area, and it will hopefully empower them to leverage and develop methods in multi-source data fusion and analytics with applications to a variety of scenarios.Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion;Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, smarter transportation and social media, among others;Provides a high-level overview of advances in this emerging field and empowers the reader to explore novel applications and methodologies that would enrich the field
()See also:Semi Supervised Learning
20
Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark
Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest.
...moreMachine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest.
You’ll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classificationAfter reading this book, you will understand how to use PySpark’s machine learning library to build and train various machine learning models. Additionally you’ll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applicationsWhat You Will LearnBuild a spectrum of supervised and unsupervised machine learning algorithmsImplement machine learning algorithms with Spark MLlib librariesDevelop a recommender system with Spark MLlib librariesHandle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit modelWho This Book Is For Data science and machine learning professionals
()21
Master various domains of machine learning with real-world projects using TensorFlow for R, H2O, MXNet and more
Key Features
Master machine learning, deep learning, and predictive modeling concepts in R 3.5
Build end-to-end projects for real-world domains such as finance, retail, social media, and more
...moreKey Features
Master machine learning, deep learning, and predictive modeling concepts in R 3.5
Build end-to-end projects for real-world domains such as finance, retail, social media, and more
An expert guide to implementing smart cognitive models with helpful tips and best practicesBook DescriptionR is one of the preferred languages when it comes to ease statistical computing and help to explore the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications to carry out intelligent tasks within your organizationThis book will help you test your knowledge and skills, guiding you to build easy-to-complex machine learning projects. You will first learn to build powerful Machine learning models through Ensembles to Estimate Credit Risk. Moving on, you will learn to implement a Bank Products Recommendation Engine and learn to perform Sentiment Analysis on Amazon Reviews to analyze the customer reviews. Next, you will explore the different clustering techniques to segment the customers using E-Commerce data. You will also learn credit card fraud detection using Autoencoders and Reinforcement Learning to make your own Robot and navigate itBy the end of the book, you will be able to carry out complex tasks to build research or commercial projects to carry out automated tasksWhat you will learnImplement Deep Neural Networks and various frameworks that can be used in RCreate a bank product recommendation engine to recommend quality productsBuild powerful Machine Learning models through Ensembles to Estimate Credit RiskLearn how to build Autoencoders for credit card fraud detectionHands-on experience with image recognition and Convolutional Neural NetworksGet to grips with reinforcement learning application for robotsImplement NLP techniques for sentiment analysis, and customer segmentationWho This Book Is ForThis book is for data analysts, data scientists, and machine learning developers who wish to master the machine learning concepts using R by building real-world projects. Each project will help you test your expertise to implement the working mechanism of machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is a must
()See also:Tensorflow, Reinforcement Learning
22
Learn the technology that's the backbone of self-driven cars, facial recognition programs, and several other exciting services and applications
Key Features
Get a hands-on, fast paced introduction to Python
Understand various applied machine learning concepts with real life examples
...moreKey Features
Get a hands-on, fast paced introduction to Python
Understand various applied machine learning concepts with real life examples
Implement a supervised machine learning pipeline from data ingest to validationBook DescriptionApplied Supervised Learning with Python provides you a rich understanding of machine learning, one of the most pursued topics in information science, and Python, one of the most popular scripting languages. Through this book, you'll learn Jupyter Notebooks, the technology used in academic and commercial circles with in-line code running supportYou'll begin the book with an overview of supervised machine learning by looking at some intuitive and real-world examples. You'll study the tools and libraries that Python offers for machine learning. Then, you'll learn the basics of data science in Python, such as slicing, filtering, and merging. You'll explore various techniques used for assessing data quality, learn about regression models, and explore their applications. You'll also study classification and explore concepts, such as K-nearest neighbor classification and decision trees. You'll learn the famous ID3 algorithm that recursively forms splits data to make accurate decision trees. Next on the agenda will be neural networks, the internals of these models, why they work, and how you can modify different architectures to maximize performance. You'll find out more about ensemble modeling, Random Forest classifier, and other methods for combining the results from multiple models. Finally, you'll learn cross-validation to test your algorithm and check how well the model works on data it hasn't 'seen'With this book, you'll be equipped to work with machine learning algorithms or create some of your own!What you will learnUnderstand the concept of supervised learning and its applicationsUse machine learning Python libraries to build your supervised learning algorithmsValidate models using k-foldUse xgboost to train and show performance of your modelUnderstand and work with decision treesUse ensemble modelling techniques to create newer modelsApply various metrics to compare machine learning models Who This Book Is ForApplied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help you to have some experience in any functional or object oriented language and have a basic understanding of Pythonic libraries and expressions, such as arrays and dictionaries
()See also:Data Science, Machine Learning Model
23
The research work has a notable social impact as it facilitates liver cancer diagnosis on the basis of statistical approaches and experimental performance of machine learning classifiers on ILPD(Indian Liver Patient Dataset) and BUPA liver datasets. The work embodies certain discovered facts. The liver cancer diagnosis can be governed by concept learning, artificial neural modeling, geometric distribution and Cobb-Douglas model. The augmentation or expansion of features indicating liver cancer growth can be quantified and realized based on Markov property based state transition.
...moreLiver cancer detection can also be analyzed based upon the fundamental principle of information gain. The realibility and mean time to failure of liver cancer testing system can be carried out in the light of parallel system configuration. The factor leading to liver cancer can be sensed on the basis of weighted majority algorithms.The present objective is also to propose a method using supervised machine learning that can help the physician for accurate diagnosis of liver cancer. For experimental analysis two liver cancer datasets and six diverse classifiers in machine learning have been used.
()24
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.
Cutting-edge machine learning principles, practices, and applications
This comprehensive textbook explores the theoretical under¬pinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems.
...moreCutting-edge machine learning principles, practices, and applications
This comprehensive textbook explores the theoretical under¬pinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems.
Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning topics with special emphasis on methods that have been profitably employedCoverage includes:•Supervised learning•Statistical learning•Learning with support vector machines (SVM)•Learning with neural networks (NN)•Fuzzy inference systems•Data clustering•Data transformations•Decision tree learning•Business intelligence•Data mining•And much more
()See also:Algorithms
25
Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science
...moreData lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakesIndustrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory, supply chain, 3D printing, education, research, and data science. The book covers: supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptorsWhat You Will LearnGenerate and identify transformational disruptors of artificial intelligence (AI)Understand the field of machine learning (ML) and apply it to handle big data and process the data lakes in your environmentHone the skills required to handle the future of data engineering and data scienceWho This Book Is ForIntermediate to expert level professionals in the fields of data science, data engineering, machine learning, and data management
()26
Learn the ropes of supervised machine learning with R by studying popular real-world uses cases, such as object detection in driverless cars, customer churn, and default prediction.
Key Features
Study the supervised learning algorithms by using them on real world datasets
Fine tune optimal parameters with hyperparameter optimization
...moreKey Features
Study the supervised learning algorithms by using them on real world datasets
Fine tune optimal parameters with hyperparameter optimization
Select the best algorithm using the model evaluation frameworkBook DescriptionSupervised Learning with R covers the complete process of developing applications with supervised machine learning algorithms that cater to your business needs. Your learning curve starts with developing your analytical thinking towards creating a problem statement using business inputs or domain research. You will learn many evaluation metrics that compare various algorithms, and you can then use these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine tune your set of optimal parameters. To avoid overfitting your model, you will also be shown how to add various regularization termsWhen you complete the book, you will find yourself an expert at modelling a supervised machine learning algorithm that precisely fulfills your business needWhat you will learnDevelop analytical thinking to precisely identify a business problemWrangle data with dplyr, tidyr, and reshape2Visualize data with ggplot2Validate your supervised machine learning model using k-foldOptimize hyperparameters with grid and random search and bayesian optimizationDeploy your model on AWS Lambda with plumber, an R packageImprove the model performance with feature selection and dimensionality reductionUse a research paper with a real-world problem, frame a problem statement, and replicate its results with a supervised machine learning model Who This Book Is ForThis book is specially designed for novice and intermediate data analysts, data scientists, and data engineers who want to explore various methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this book
()See also:Machine Learning Model
27
This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications.
...morePractitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018)Includes new advances in clustering and classification using semi-supervised and unsupervised learning;Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning;Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning
()See also:Semi Supervised Learning
28
This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from big data.
...moreThe book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization
()29
As the amount of data is increasing over the tremendous rate, it is extremely viable to imply smart analysis. It deals with optimization of performance criterion dealing with examples relevant to present and past situations. Learning plays a vital role in making predictions from analysis of data set properties. We also ensure that the selected data set suits our purpose of predicting futuristic events and unseen samples. However while dealing with problems of classification in machine learning we need to determine and draw observations relevant to a problem statement having disjoint set of training data.
...moreMining information from data enrols classification, clustering and other such methodologies as its subsets. The thesis presents a classical descriptive procedure to compare various classification schemes under single roof and draw analysis over the best scorer in terms of accuracy to draw predictions of Bank Marketing success rate while targeting potential customers. Various data sets can be filtered by the approached schemes to make decision during binary and multi valued classification. However the classification model ranks one over other to determine the best fit choice.
()30
This book presents machine learning as a set of pre-requisites, co-requisites, and post-requisites, focusing on mathematical concepts and engineering applications in advanced welding and cutting processes. It describes a number of advanced welding and cutting processes and then assesses the parametrical interdependencies of two entities, namely the data analysis and data visualization techniques, which form the core of machine learning. Subsequently, it discusses supervised learning, highlighting Python libraries such as NumPy, Pandas and Scikit Learn programming.
...moreIt also includes case studies that employ machine learning for manufacturing processes in the engineering domain. The book not only provides beginners with an introduction to machine learning for applied sciences, enabling them to address global competitiveness and work on real-time technical challenges, it is also a valuable resource for scholars with domain knowledge.
()31
2 Books in 1
Do not miss out on the bundle book offer!
300+ pages of valuable content What You'll Learn
Book 1 Machine Learning
...moreDo not miss out on the bundle book offer!
300+ pages of valuable content What You'll Learn
Book 1 Machine Learning
You will learn the fundamentals of machine learning from algorithms, python, supervised and unsupervised learning Concepts such as " decision trees " & "random forest introduction" are explained in detail and come with visual diagrams to assist in grasping the subject matter You will learn real world applications of machine learning,artificial intelligence and understand how it will effect humanity in the upcoming years The world is constantly changing and evolving, transportation was revolutionized by cars and planes, however, "machine learning" will revolutionize the world in which we live from simple day to day tasks to even the most complex endeavors Book 2 Markov Models In the segment of the bundle book you will learn the mathematics behind Markov Models algorithms, artificial intelligence, weather reporting, Bayesian inference, tools, solutions and much, much more! You will gain insights to the 3 main problems of Markov Models and learn how to overcome them. You will also learn about the real world applications, implications and theories of Markov Models This is an incredible offer you do not want to miss out on! This bundle book offer gives you so much value at an affordable price you won't find anywhere else What are you waiting for? Grab your copy now Note* For the best visual experience of diagrams it is highly recommended you purchases the paperback version of the bundle book offer
()See also:Machine Learning
32
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research.
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given).
...moreIn the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given).
Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction
()See also:Machine Learning, Semi Supervised Learning
33
We've all heard of AI (artificial intelligence) but what does machine learning really mean?
The phrase “Machine Learning” refers to the automatic detection of meaningful data by computing systems. In the last few decades, it has become a common tool in almost any task that needs to understand data from large data sets. One of the biggest application of machine learning technology is the search engine. Search engines learn how to provide the best results based on historic, trending, and relative data sets. When you look at anti-spam software, it learns how to filter email messages.
...moreThe phrase “Machine Learning” refers to the automatic detection of meaningful data by computing systems. In the last few decades, it has become a common tool in almost any task that needs to understand data from large data sets. One of the biggest application of machine learning technology is the search engine. Search engines learn how to provide the best results based on historic, trending, and relative data sets. When you look at anti-spam software, it learns how to filter email messages.
Going to credit cards, transactions are secured by software that knows when fraudulent activities are going on. We currently have digital cameras that detect faces, personal assistant applications that are intelligent enough to learn voice commands. These are all applications based on machine learning!Cars are becoming equipped with accident prevention systems that are powered by machine learning algorithms. Machine learning is also widely used in scientific fields like bioinformatics and astronomy. In contrast to traditional computing, and due to the complexity of patterns that need to be detected, it is hard for a programmer to provide a fine-detailed specification on the execution of these tasks. So where do we start?How about key machine learning algorithms? These are algorithms that are used in the real world, and they give a wide spectrum of the different learning techniques. There are also different algorithms that are better suited for big data. The world has become increasingly connected, and as a result, and in many business applications, there is a lot of data and computation needed to learn different concepts.As you can imagine, the topic of machine learning, depending on the application, can be contained or wildly complex. This book will give you an overview of what machine learning is capable of and some basic algorithms to help you understand the fundamentals of the technologyFinally, how will the employment landscape going to be affected by machine learning in the near future? In later chapters of this book, we will talk about the skills that a you will need to have to work in a profession related to machine learning, and how each field might be affected by the age of computerization. The future is changing very quickly and professionals will need to adapt to ever-evolving technology if they want to stand a chance in keeping up with the joneses
()See also:Machine Learning
34
This book constitutes thoroughly refereed revised selected papers from the First IAPR TC3 Workshop on Partially Supervised Learning, PSL 2011, held in Ulm, Germany, in September 2011. The 14 papers presented in this volume were carefully reviewed and selected for inclusion in the book, which also includes 3 invited talks. PSL 2011 dealt with methodological issues as well as real-world applications of PSL.
...moreThe main methodological issues were: combination of supervised and unsupervised learning; diffusion learning; semi-supervised classification, regression, and clustering; learning with deep architectures; active learning; PSL with vague, fuzzy, or uncertain teaching signals; learning, or statistical pattern recognition; and PSL in cognitive systems. Applications of PSL included: image and signal processing; multi-modal information processing; sensor/information fusion; human computer interaction; data mining and Web mining; forensic anthropology; and bioinformatics.
()35
This is an indispensable scientific text book brings the beginners right to the forefront of the in-depth fundamentals of the traditional data mining classification techniques for the analysis of the neuro-degenerative disease with special reference to the Alzheimer's Disease. Beginning with an overview of the alzheimer's disease, this text book analyses the bio markers and the classification techniques in detail. Then follows an in-depth treatment of the better literature coverage.
...moreThis book is ideally suited for the researchers of Computer Science and the researchers will benefit from the comprehensive wide range of literature coverage and the better explanations on cutting-edge material.
()36
This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering
Key Features
Your handy guide to building machine learning workflows in Go for real-world scenarios
Build predictive models using the popular supervised and unsupervised machine learning techniques
...moreKey Features
Your handy guide to building machine learning workflows in Go for real-world scenarios
Build predictive models using the popular supervised and unsupervised machine learning techniques
Learn all about deployment strategies and take your ML application from prototype to production ready Book Description Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones What you will learn Understand the types of problem that machine learning solves, and the various approaches Import, pre-process, and explore data with Go to make it ready for machine learning algorithms Visualize data with gonum/plot and Gophernotes Diagnose common machine learning problems, such as overfitting and underfitting Implement supervised and unsupervised learning algorithms using Go libraries Build a simple web service around a model and use it to make predictions Who this book is for This book is for developers and data scientists with at least beginner-level knowledge of Go, and a vague idea of what types of problem Machine Learning aims to tackle. No advanced knowledge of Go (and no theoretical understanding of the math that underpins Machine Learning) is requiredTable of Contents Introducing Machine Leaning with Go Setting Up the Development Environment Supervised Learning Unsupervised Learning Using Pretrained Models Deploying Machine Learning Applications Conclusion - Successful ML Projects
()37
Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled "training" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).
...moreThis book delves into the following topics: •Linear discriminant analysis •Quadratic discriminant analysis •Decision trees, decision lists •Kernel estimation and K-nearest-neighbor algorithms •Naive Bayes classifier •Neural networks •Support vector machines .Boosting, Random Forest and Bagging
()38
The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models (this book develops classification techniques).Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses.
...moreClustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition.
()See also:Decision Tree
39
Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. Unsupervised learning is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition.
...moreMATLAB provides tools to help you try out a variety of machine learning models and choose the best. Multivariate statistical techniques include supervised and unsupervised learning techniques. This book develops unsupervised learning techniques such as cluster analysis, dimension reduction, multidimensional scaling and feature selection.
()See also:Machine Learning Model
40
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples.
The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters
...moreThe book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters
You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting waysWhat You'll LearnReview the new features of TensorFlow 2.0Use TensorFlow 2.0 to build machine learning and deep learning modelsPerform sequence predictions using TensorFlow 2.0Deploy TensorFlow 2.0 models with practical examplesWho This Book Is ForData scientists, machine and deep learning engineers
()See also:Tensorflow
41
Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
...moreThe aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models.•Classification techniques predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, image and speech recognition, and credit scoring. This book develops Supervised Machine Learning Techniques for Classification.•Regression techniques predict continuous responses, for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.
()See also:Matlab
42
An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises.
This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises.
...moreThis book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises.
Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book drawsThe book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment
()See also:Reinforcement Learning
43
Many industry experts consider unsupervised learning the next AI frontier, one that may hold the key to general artificial intelligence. Armed with the conceptual knowledge in this book, data scientists and machine learning practitioners will learn hands-on how to apply unsupervised learning to large unlabeled datasets using Python tools. You’ll uncover hidden patterns, gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets
...moreAuthor Ankur Patel—an applied machine-learning researcher and data scientist with expertise in financial markets—provides the concepts, intuition, and tools necessary for you to apply this technology to problems you tackle every day. Through the course of this book, you’ll learn how to build production-ready systems with PythonChapters for this Early Release edition will be available as they are completed, including the first five chapters in this initial release:Examine the difference between supervised and unsupervised learning, and the relative strengths and weaknesses of eachSet up and manage a machine learning project end-to-end—everything from data acquisition to building a model and implementing a solution in productionExplore dimensionality reduction algorithms that learn the underlying structure of a dataset’s most salient informationBuild a credit card fraud detection system using dimensionality reduction methods
()See also:Machine Learning Model
44
This book provides different mathematical frameworks for addressing supervised learning. It is based on a workshop held under the auspices of the Center for Nonlinear Studies at Los Alamos and the Santa Fe Institute in the summer of 1992.
...more ()
Choose a different view: