Ensure optimal care experiences for patients and create efficient operations behind-the-scenes.
Ces promotions seront appliquées à cet article : Certaines promotions sont cumulables avec d'autres offres promotionnelles, d'autres non. The first one, Data Mining for the Masses by Matthew North, is a very practical book for beginners and intermediate data miners (and is available for free here), whereas The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman provides a deep insight into the mathematical models driving the heart of every data analysis. Seuls des destinataires résidant dans votre pays peuvent récupérer un ebook offert. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems. SVBook Pte. The book is targeted at readers with a statistical, mathematical or informatics background who want to understand not only how to use an operator in RapidMiner, but also why it works. Follow RapidMiner -- Data Mining, ETL, OLAP, BI. Sélectionnez la section dans laquelle vous souhaitez faire votre recherche. thanks for posting the web links on Data Mining for Human Resources. Easily blend and join data from a variety of sources including relational databases, NoSQL, APIs, spreadsheets, applications, social media, and more. The book is downloadable from the author’s website. Being not only a company but even an open source company allows us to share this great feeling even more often. From brand communities to product support boards, forums are a great way to engage and provide value to your audience.
(This may not be possible with some types of ads). Ltd. offers affordable, easy to read Data Mining, Text Mining, Data Analytics, Programming books for professional and lifelong learning. Because of the easy style of writing and the good examples, this book is suited not only for IT professionals and college students who want to take a deeper look onto data mining, but for anyone who wants to learn how to get the most out of their data. Ingo Mierswa is the founder and president of RapidMiner and an industry-veteran data scientist since starting to develop RapidMiner at the Artificial Intelligence Division of the TU Dortmund University in Germany.
© 2020 RapidMiner, Inc. All rights Reserved. Markus Hofmann is a lecturer at the Institute of Technology Blanchardstown, where he focuses on data mining, text mining, data exploration and visualization, and business intelligence. In this RapidMiner tutorial, you will learn how to use RapidMiner for data mining. Les promotions s'appliquent lorsque vous achetez. Speed up and automate the creation of predictive models in a drag + drop visual interface, Pre-built templates for common use cases including customer churn, predictive maintenance, fraud detection, and many more, “Wisdom of Crowds” provides proactive recommendations at every step to help beginners, Instantly create point + click connections to databases, enterprise data warehouses, data lakes, cloud storages, business applications and social media, Easily re-use connections any time and easily share them with anyone who needs access, Connect to new sources with extensions from the, Query and retrieve data without writing complex SQL, Harness the power of highly scalable database clusters, Supports MySQL, PostgreSQL, and Google BigQuery, Understand patterns, trends, and distributions with scatter plots, histograms, line charts, parallel coordinates, box plots, and more, Quickly find and fix common data quality problems including missing values and outliers, Explore data using robust statistical overviews and over 30 interactive visualizations, Extract, join, filter, and group data across any number of sources, Create repeatable data prep and ETL processes that can be scheduled and shared, Choose from hundreds of supervised and unsupervised machine learning algorithms, Implement basic and advanced ML techniques including regression, clustering, time-series, text analytics, and deep learning, Build the model to be sensitive to constraints like costs to optimize the predictions for your desired business impact, Use both automated and manual feature engineering to optimize model accuracy, Eliminate overfitting through a unique approach that prevents model training pre-processing data from leaking into the application of the model, Add proven techniques, like cross validation, to a model with just a single mouse click, Each step in the data prep, modeling, and validation process is documented for complete transparency, Visuals are easy to explain to others in the organization to gain buy-in for deployment, Supports the Local Interpretable Model-Agnostic Explanations (LIME) framework, Deploy code-based models and code-containing models into a scalable platform, Eliminate duplicate work and ensure others re-use your work by uploading your code snippets into the RapidMiner repository for use in a simplified visual workflow designer, Make use of the vibrant Python ecosystem by building on top of specialized and cutting-edge libraries and making it available for the rest of your team using RapidMiner, Leverage data prep and ETL pipelines others have produced, Quickly deploy scored data to spreadsheets and data visualization tools or turn models into production web services with RapidMiner AI Hub (formerly RapidMiner Server), Integrate with enterprise scheduling tools, Use process control operators to create workflows that repeat and loop over tasks, branch flows and access system resources, Supports a variety of scripting languages for custom integrations and automatons, Use R and Python code and libraries to extend RapidMiner, Integrate RapidMiner with Python through the, Download new functionality provided by RapidMiner, our partners, and community through the, Add new capabilities to RapidMiner through its flexible extension mechanism, RapidMiner Studio open core is available under the.
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