Jump-start your career as a data scientist learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most.
Implement decentralized blockchain applications to build scalable DappsKey FeaturesUnderstand the blockchain ecosystem and its terminologiesImplement smart contracts, wallets, and consensus protocolsDesign and develop decentralized applications using Bitcoin, Ethereum, and HyperledgerBook DescriptionThe Blockchain is a revolution promising a new world without middlemen.
Build, design and improve advanced business intelligence solutions using Tableau's latest features, including Tableau Prep, Tableau Hyper, and Tableau ServerKey FeaturesMaster new features in Tableau 2019.
Build efficient, high-performance & scalable systems to process large volumes of data with Apache IgniteKey FeaturesUnderstand Apache Ignite's in-memory technologyCreate High-Performance app components with IgniteBuild a real-time data streaming and complex event processing systemBook DescriptionApache Ignite is a distributed in-memory platform designed to scale and process large volume of data.
Pattern recognition is a fast growing area with applications in a widely diverse number of fields such as communications engineering, bioinformatics, data mining, content-based database retrieval, to name but a few.
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problemsKey FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook DescriptionMachine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners.
Cultivating Professional Identity in Design is a nuanced, comprehensive companion for designers across disciplines honing their identities, self-perception, personal strengths, and essential attributes.
Stochastic Modeling and Statistical Methods: Advances and Applications is the practical guide to the latest developments in data analysis and research methods.
Explore Kinesis managed services such as Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and Kinesis Video Streams with the help of practical use casesKey FeaturesGet well versed with the capabilities of Amazon KinesisExplore the monitoring, scaling, security, and deployment patterns of various Amazon Kinesis servicesLearn how other Amazon Web Services and third-party applications such as Splunk can be used as destinations for Kinesis dataBook DescriptionAmazon Kinesis is a collection of secure, serverless, durable, and highly available purpose-built data streaming services.
Modeling and Control of Drug Delivery Systems provides comprehensive coverage of various drug delivery and targeting systems and their state-of-the-art related works, ranging from theory to real-world deployment and future perspectives.
Data Analysis Methods in Physical Oceanography is a practical referenceguide to established and modern data analysis techniques in earth and oceansciences.
Leverage the power of Python and statistical modeling techniques for building accurate predictive modelsKey FeaturesGet introduced to Python's rich suite of libraries for statistical modelingImplement regression, clustering and train neural networks from scratchIncludes real-world examples on training end-to-end machine learning systems in PythonBook DescriptionPython's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today.
Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and moreKey FeaturesMaster machine learning, deep learning, and predictive modeling concepts in R 3.
Tackle the complex challenges faced while building end-to-end deep learning models using modern R librariesKey FeaturesUnderstand the intricacies of R deep learning packages to perform a range of deep learning tasksImplement deep learning techniques and algorithms for real-world use casesExplore various state-of-the-art techniques for fine-tuning neural network modelsBook DescriptionDeep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning.
This book discusses the multifaceted nature of organizational excellence and proceeds to consider the roles played by data, technological advances leading to innovation and value-based leadership in guiding an organization in its journey towards excellence.
Databases for Data-Centric Geotechnics forms a definitive reference and guide to databases in geotechnical and rock engineering, to enhance decision-making in geotechnical practice using data-driven methods.
Although, the basic concept of a fuel cell is quite simple, creating new designs and optimizing their performance takes serious work and a mastery of several technical areas.
With the aim of simplifying relational database modeling, Database Modeling Step-by-Step presents the standard approach to database normalization and then adds its own approach, which is a more simplistic, intuitive way to building relational database models.
For many small businesses, organisations, clubs, artists, faith groups, voluntary organisations/charities and sole traders, applying the General Data Protection Regulation (GDPR) has been like playing a game of "e;Snakes and Ladders"e;.
Get up and running with machine learning life cycle management and implement MLOps in your organizationKey FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook DescriptionEngineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.
Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible Universal Resource Identifiers as a basis for information sharing.
The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements.
Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges.
Provides a one-stop resource for engineers learning biostatistics using MATLAB and WinBUGS Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing bio-oriented engineering fields while implementing software packages that are familiar to engineers.
A concise guide to the theory and application of numerical methods for predicting ocean acoustic propagation, also providing an introduction to numerical methods, with an overview of those methods presently in use.
New computational design tools have evolved rapidly and been increasingly applied in the field of design in recent years, complimenting and even replacing the traditional design media and approaches.
Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant.
