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Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
by Chris AlbonThis practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics.Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.You’ll find recipes for:Vectors, matrices, and arraysHandling numerical and categorical data, text, images, and dates and timesDimensionality reduction using feature extraction or feature selectionModel evaluation and selectionLinear and logical regression, trees and forests, and k-nearest neighborsSupport vector machines (SVM), naïve Bayes, clustering, and neural networksSaving and loading trained models
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
by Chris Albon Kyle GallatinThis practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks.Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure that it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context.Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications. You'll find recipes for:Vectors, matrices, and arraysWorking with data from CSV, JSON, SQL, databases, cloud storage, and other sourcesHandling numerical and categorical data, text, images, and dates and timesDimensionality reduction using feature extraction or feature selectionModel evaluation and selectionLinear and logical regression, trees and forests, and k-nearest neighborsSupporting vector machines (SVM), naäve Bayes, clustering, and tree-based modelsSaving, loading, and serving trained models from multiple frameworks
Machine Learning with R
by Brett LantzWritten as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
Machine Learning with R - Second Edition
by Brett LantzPerhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly.<P><P> It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
Machine Learning with R - Third Edition
by Brett LantzData scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.
Machine Learning with R Cookbook
by Chiu Yu-Wei<P><P>Key Features <P><P>Apply R to simplify predictive modeling with short and simple code <P><P>Use machine learning to solve problems ranging from small to big data <P><P>Build a training and testing dataset from the churn dataset, applying different classification methods <P><P>Book Description <P><P>The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. <P><P>This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction. <P><P>What you will learn <P><P>Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm <P><P>Visualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithm <P><P>Compare differences between each regression method to discover how they solve problems <P><P>Predict possible churn users with the classification approach <P><P>Implement the clustering method to segment customer data <P><P>Compress images with the dimension reduction method <P><P>Incorporate R and Hadoop to solve machine learning problems on Big Data
Machine Learning with R Cookbook
by Yu-Wei ChiuIf you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.
Machine Learning with R Cookbook, Second Edition
by AshishSingh Bhatia<P><P>Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R code <P><P>About This Book <P><P>Apply R to simplify predictive modeling with short and simple code <P><P>Use machine learning to solve problems ranging from small to big data <P><P>Build a training and testing dataset, applying different classification methods. <P><P>Who This Book Is For <P><P>This book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful. <P><P>What You Will Learn <P><P>Create and inspect transaction datasets and perform association analysis with the Apriori algorithm <P><P>Visualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithm <P><P>Compare differences between each regression method to discover how they solve problems <P><P>Detect and impute missing values in air quality data <P><P>Predict possible churn users with the classification approach <P><P>Plot the autocorrelation function with time series analysis <P><P>Use the Cox proportional hazards model for survival analysis <P><P>Implement the clustering method to segment customer data <P><P>Compress images with the dimension reduction method <P><P>Incorporate R and Hadoop to solve machine learning problems on big data <P><P>In Detail <P><P>Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.
Machine Learning with R Quick Start Guide: A beginner's guide to implementing machine learning techniques from scratch using R 3.5
by Ivan Pastor SanzLearn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more. Key Features Use R 3.5 to implement real-world examples in machine learning Implement key machine learning algorithms to understand the working mechanism of smart models Create end-to-end machine learning pipelines using modern libraries from the R ecosystem Book Description Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R. What you will learn Introduce yourself to the basics of machine learning with R 3.5 Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results Learn to build predictive models with the help of various machine learning techniques Use R to visualize data spread across multiple dimensions and extract useful features Use interactive data analysis with R to get insights into data Implement supervised and unsupervised learning, and NLP using R libraries Who this book is for This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.
Machine Learning with R Second Edition
by Brett Lantz<P><P>Build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R <P><P>Key Features <P><P>Harness the power of R for statistical computing and data science <P><P>Explore, forecast, and classify data with R <P><P>Use R to apply common machine learning algorithms to real-world scenarios <P><P>Book Description <P><P>Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of R's cross-platform, zero-cost statistical programming environment, there has never been a better time to start applying machine learning to your data. Machine learning with R offers a powerful set of methods to quickly and easily gain insight from your data to both, veterans and beginners in data analytics. <P><P>Want to turn your data into actionable knowledge, predict outcomes that make real impact, and have constantly developing insights? R gives you access to all the power you need to master exceptional machine learning techniques. <P><P>The second edition of Machine Learning with R provides you with an introduction to the essential skills required in data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. <P><P>With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn to to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R. <P><P>What you will learn <P><P>Harness the power of R to build common machine learning algorithms with real-world data science applications <P><P>Get to grips with techniques in R to clean and prepare your data for analysis and visualize your results <P><P>Discover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems <P><P>Classify your data with Bayesian and nearest neighbour methods <P><P>Predict values using R to build decision trees, rules, and support vector machines <P><P>Forecast numeric values with linear regression and model your data with neural networks <P><P>Evaluate and improve the performance of machine learning models <P><P>Learn specialized machine learning techniques for text mining, social network data, and big data
Machine Learning with R, the tidyverse, and mlr
by Hefin RhysSummary Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of academic data scientists, machine learning has become a mainstream business process, and tools like the easy-to-learn R programming language put high-quality data analysis in the hands of any programmer. Machine Learning with R, the tidyverse, and mlr teaches you widely used ML techniques and how to apply them to your own datasets using the R programming language and its powerful ecosystem of tools. This book will get you started! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning with R, the tidyverse, and mlr gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics and easy-to-grasp examples. In each engaging chapter, you&’ll put a new algorithm into action to solve a quirky predictive analysis problem, including Titanic survival odds, spam email filtering, and poisoned wine investigation. What's inside Using the tidyverse packages to process and plot your data Techniques for supervised and unsupervised learning Classification, regression, dimension reduction, and clustering algorithms Statistics primer to fill gaps in your knowledge About the reader For newcomers to machine learning with basic skills in R. About the author Hefin I. Rhys is a senior laboratory research scientist at the Francis Crick Institute. He runs his own YouTube channel of screencast tutorials for R and RStudio. Table of contents: PART 1 - INTRODUCTION 1.Introduction to machine learning 2. Tidying, manipulating, and plotting data with the tidyverse PART 2 - CLASSIFICATION 3. Classifying based on similarities with k-nearest neighbors 4. Classifying based on odds with logistic regression 5. Classifying by maximizing separation with discriminant analysis 6. Classifying with naive Bayes and support vector machines 7. Classifying with decision trees 8. Improving decision trees with random forests and boosting PART 3 - REGRESSION 9. Linear regression 10. Nonlinear regression with generalized additive models 11. Preventing overfitting with ridge regression, LASSO, and elastic net 12. Regression with kNN, random forest, and XGBoost PART 4 - DIMENSION REDUCTION 13. Maximizing variance with principal component analysis 14. Maximizing similarity with t-SNE and UMAP 15. Self-organizing maps and locally linear embedding PART 5 - CLUSTERING 16. Clustering by finding centers with k-means 17. Hierarchical clustering 18. Clustering based on density: DBSCAN and OPTICS 19. Clustering based on distributions with mixture modeling 20. Final notes and further reading
Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
by Brett LantzSolve real-world data problems with R and machine learningKey FeaturesThird edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyondHarness the power of R to build flexible, effective, and transparent machine learning modelsLearn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett LantzBook DescriptionMachine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.What you will learnDiscover the origins of machine learning and how exactly a computer learns by examplePrepare your data for machine learning work with the R programming languageClassify important outcomes using nearest neighbor and Bayesian methodsPredict future events using decision trees, rules, and support vector machinesForecast numeric data and estimate financial values using regression methodsModel complex processes with artificial neural networks — the basis of deep learningAvoid bias in machine learning modelsEvaluate your models and improve their performanceConnect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlowWho this book is forData scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.
Machine Learning with R: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data, 4th Edition
by Brett LantzLearn how to solve real-world data problems using machine learning and RPurchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesThe 10th Anniversary Edition of the bestselling R machine learning book, updated with 50% new content for R 4.0.0 and beyondHarness the power of R to build flexible, effective, and transparent machine learning modelsLearn quickly with this clear, hands-on guide by machine learning expert Brett LantzBook DescriptionMachine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Fourth Edition, provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data.You'll also find this classic R data science book updated to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read that will help you find powerful new insights in your data.What you will learnLearn the end-to-end process of machine learning from raw data to implementationClassify important outcomes using nearest neighbor and Bayesian methodsPredict future events using decision trees, rules, and support vector machinesForecast numeric data and estimate financial values using regression methodsModel complex processes with artificial neural networksPrepare, transform, and clean data using the tidyverseEvaluate your models and improve their performanceConnect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlowWho this book is forThis book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful.
Machine Learning with Scala Quick Start Guide: Leverage popular machine learning algorithms and techniques and implement them in Scala
by Md. Rezaul KarimSupervised and unsupervised machine learning made easy in Scala with this quick-start guide.Key FeaturesConstruct and deploy machine learning systems that learn from your data and give accurate predictionsUnleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala.Solve hands-on problems by combining popular neural network architectures such as LSTM and CNN using Scala with DeepLearning4j libraryBook DescriptionScala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala.The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms.It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.What you will learnGet acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4jLearn RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured dataUnderstand supervised and unsupervised learning techniques with best practices and pitfallsLearn classification and regression analysis with linear regression, logistic regression, Naïve Bayes, support vector machine, and tree-based ensemble techniques Learn effective ways of clustering analysis with dimensionality reduction techniquesLearn recommender systems with collaborative filtering approachDelve into deep learning and neural network architecturesWho this book is forThis book is for machine learning developers looking to train machine learning models in Scala without spending too much time and effort. Some fundamental knowledge of Scala programming and some basics of statistics and linear algebra is all you need to get started with this book.
Machine Learning with Spark
by Nick PentreathIf you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you. While it may be useful to have a basic understanding of Spark, no previous experience is required.
Machine Learning with Spark - Second Edition
by Rajdeep DuaIf you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.
Machine Learning with Spark and Python: Essential Techniques for Predictive Analytics
by Michael BowlesMachine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark—a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code. Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.
Machine Learning with Swift: Artificial Intelligence for iOS
by Oleksandr Sosnovshchenko Oleksandr BaievLeverage the power of machine learning and Swift programming to build intelligent iOS applications with ease Key Features Implement effective machine learning solutions for your iOS applications Use Swift and Core ML to build and deploy popular machine learning models Develop neural networks for natural language processing and computer vision Book Description Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves. What you will learn Learn rapid model prototyping with Python and Swift Deploy pre-trained models to iOS using Core ML Find hidden patterns in the data using unsupervised learning Get a deeper understanding of the clustering techniques Learn modern compact architectures of neural networks for iOS devices Train neural networks for image processing and natural language processingWho this book is for iOS developers who wish to create smarter iOS applications using the power of machine learning will find this book to be useful. This book will also benefit data science professionals who are interested in performing machine learning on mobile devices. Familiarity with Swift programming is all you need to get started with this book.
Machine Learning with TensorFlow 1.x
by Quan Hua Shams Ul Azeem Saif AhmedAbout This BookEnter the new era of second-generation machine learning with Python with this practical and insightful guideSet up TensorFlow 1.x for actual industrial use, including high-performance setup aspects such as multi-GPU supportCreate pipelines for training and using applying classifiers using raw real-world dataWho This Book Is ForThis book is for data scientists and researchers who are looking to either migrate from an existing machine learning library or jump into a machine learning platform headfirst. The book is also for software developers who wish to learn deep learning by example. Particular focus is placed on solving commercial deep learning problems from several industries using TensorFlow's unique features. No commercial domain knowledge is required, but familiarity with Python and matrix math is expected.What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build deep neural networks using TensorFlow 1.xCover key tasks such as clustering, sentiment analysis, and regression analysis using TensorFlow 1.xFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityLearn how to use multiple GPUs for faster training using AWSIn DetailGoogle's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x.Firstly, you'll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You'll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you'll implement a complete real-life production system from training to serving a deep learning model. As you advance you'll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you'll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.
Machine Learning with oneAPI
by Shriram K. Vasudevan Sini Raj Pulari T.S. Murugesh Nitin Vamsi DantuoneAPI is a unified programming model and software development kit (SDK) from Intel that empowers software developers to generate high-performance applications that can run on different devices, comprising CPUs, GPUs, FPGAs, and other accelerators. It lets developers write code once and deploy it on multiple architectures, decreasing the complexity as well as the cost and time of software development. One of the significant strengths of oneAPI is in its capability to support an eclectic range of devices and architectures, including artificial intelligence, high-performance computing, and data analytics. Along with libraries, tools, and compilers, oneAPI makes it cool for developers to create optimized code for an extensive variety of applications, making it an indispensable tool for any developer who wants to create high-performance software and reap the benefit of the latest hardware technologies. The versatility of oneAPI, by means of appropriate theory and practical implementation with the latest tools in machine learning, has been presented in a simple yet effective way in this book that caters to everyone’s needs. Come on, let’s unleash the true power of our code across varied architectures!
Machine Learning with scikit-learn Quick Start Guide: Classification, regression, and clustering techniques in Python
by Kevin JollyDeploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.Key FeaturesBuild your first machine learning model using scikit-learnTrain supervised and unsupervised models using popular techniques such as classification, regression and clusteringUnderstand how scikit-learn can be applied to different types of machine learning problemsBook DescriptionScikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.What you will learnLearn how to work with all scikit-learn's machine learning algorithmsInstall and set up scikit-learn to build your first machine learning modelEmploy Unsupervised Machine Learning Algorithms to cluster unlabelled data into groupsPerform classification and regression machine learningUse an effective pipeline to build a machine learning project from scratchWho this book is forThis book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.
Machine Learning with the Elastic Stack: Expert techniques to integrate machine learning with distributed search and analytics
by Bahaaldine Azarmi Rich CollierLeverage Elastic Stack's machine learning features to gain valuable insight from your data Key Features Combine machine learning with the analytic capabilities of Elastic Stack Analyze large volumes of search data and gain actionable insight from them Use external analytical tools with your Elastic Stack to improve its performance Book Description Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly. What you will learn Install the Elastic Stack to use machine learning features Understand how Elastic machine learning is used to detect a variety of anomaly types Apply effective anomaly detection to IT operations and security analytics Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting Combine your created jobs to correlate anomalies of different layers of infrastructure Learn various tips and tricks to get the most out of Elastic machine learning Who this book is for If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.
Machine Learning with the Elastic Stack: Gain valuable insights from your data with Elastic Stack's machine learning features, 2nd Edition
by Bahaaldine Azarmi Rich Collier Camilla MontonenDiscover expert techniques for combining machine learning with the analytic capabilities of Elastic Stack and uncover actionable insights from your dataKey FeaturesIntegrate machine learning with distributed search and analyticsPreprocess and analyze large volumes of search data effortlesslyOperationalize machine learning in a scalable, production-worthy wayBook DescriptionElastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection. The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with. By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.What you will learnFind out how to enable the ML commercial feature in the Elastic StackUnderstand how Elastic machine learning is used to detect different types of anomalies and make predictionsApply effective anomaly detection to IT operations, security analytics, and other use casesUtilize the results of Elastic ML in custom views, dashboards, and proactive alertingTrain and deploy supervised machine learning models for real-time inferenceDiscover various tips and tricks to get the most out of Elastic machine learningWho this book is forIf you're a data professional looking to gain insights into Elasticsearch data without having to rely on a machine learning specialist or custom development, then this Elastic Stack machine learning book is for you. You'll also find this book useful if you want to integrate machine learning with your observability, security, and analytics applications. Working knowledge of the Elastic Stack is needed to get the most out of this book.
Machine Learning with the Raspberry Pi: Experiments with Data and Computer Vision
by Donald J. NorrisUsing the Pi Camera and a Raspberry Pi board, expand and replicate interesting machine learning (ML) experiments. This book provides a solid overview of ML and a myriad of underlying topics to further explore. Non-technical discussions temper complex technical explanations to make the hottest and most complex topic in the hobbyist world of computing understandable and approachable.Machine learning, also commonly referred to as deep learning (DL), is currently being integrated into a multitude of commercial products as well as widely being used in industrial, medical, and military applications. It is hard to find any modern human activity, which has not been "touched" by artificial intelligence (AI) applications. Building on the concepts first presented in Beginning Artificial Intelligence with the Raspberry Pi, you’ll go beyond simply understanding the concepts of AI into working with real machine learning experiments and applying practical deep learning concepts to experiments with the Pi board and computer vision. What you learn with Machine Learning with the Raspberry Pi can then be moved on to other platforms to go even further in the world of AI and ML to better your hobbyist or commercial projects.What You'll LearnAcquire a working knowledge of current ML Use the Raspberry Pi to implement ML techniques and algorithmsApply AI and ML tools and techniques to your own work projects and studiesWho This Book Is ForEngineers and scientists but also experienced makers and hobbyists. Motivated high school students who desire to learn about ML can benefit from this material with determination.
Machine Learning, Advances in Computing, Renewable Energy and Communication: Proceedings of MARC 2020 (Lecture Notes in Electrical Engineering #768)
by Pramod Kumar Atif Iqbal Hasmat Malik Anuradha TomarThis book gathers selected papers presented at International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication (MARC 2020), held in Krishna Engineering College, Ghaziabad, India, during December 17–18, 2020. This book discusses key concepts, challenges, and potential solutions in connection with established and emerging topics in advanced computing, renewable energy, and network communications.