Machine learning

See how data science can answer the questions your business faces!

See how data science can answer the questions your business faces!

Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance.

Explore Snowflake's core concepts and unique features that differentiates it from industry competitors, such as, Azure Synapse and Google BigQuery.

Business intelligence applications are of vital importance as they help organizations manage, develop, and communicate intangible assets such as information and knowledge.

Understand how neural networks work and learn how to implement them using TensorFlow 2.

This book will teach you how to build recommender systems with machine learning algorithms using Python.

This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software.

This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code.

Artificial Intelligence and Edge Computing for Sustainable Ocean Health explores the transformative role of AI and edge computing in preserving and enhancing ocean health.

This interdisciplinary book incorporates various aspects of environment, ecology, and natural disaster management including cognitive informatics and computing.

Membrane computing is a class of distributed and parallel computing models inspired by living cells.

Deep learning has significantly reshaped a variety of technologies, such as image processing, natural language processing, and audio processing.

This book systematically elaborates on the intelligent information processing technology for a bioinspired polarization compass and inertial integrated navigation system.

The PC era is giving way to a new form of popular computing in which smart, globally-connected objects and environments are the new computational ground.

This book explores how visualization provides an effective way of improving not only the interpretability but also the generalization capabilities of machine learning models.

Our physical world is grounded in three dimensions.

Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning.

Anomaly detection in video surveillance stands at the core of numerous real-world applications that have broad impact and generate significant academic and industrial value.

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite.

Take a journey toward discovering, learning, and using Apache Spark 3.

Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning.

Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework.

Learn to develop and deploy dashboards as web apps using the Python programming language, and how to integrate algorithms into web apps.

Use this book as an on-ramp to the development of enterprise-grade chatbots and digital assistants.

Get introduced to ML.

Use TensorFlow 2.

Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning.

While Robotic Process Automation (RPA) has been around for about 20 years, it has hit an inflection point because of the convergence of cloud computing, big data and AI.

What is an artificial intelligence (AI)-enabled drone and what can it do?

Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species.

Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species.

Use TensorFlow 2.

Apply machine and deep learning to solve some of the challenges in the oil and gas industry.

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python.

Use artificial intelligence (AI) techniques to build tools for auditing your organization.

Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure.

Learn about Robotic Processing Automation (RPA) and how to build bots using UiPath.

Design and develop end-to-end, production-grade computer vision projects for real-world industry problems.

So far, little effort has been devoted to developing practical approaches on how to develop and deploy AI systems that meet certain standards and principles.

Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle's Autonomous Database cloud offering.