Machine learning

The emergence of artificial intelligence (AI) has brought us to the precipice of a new age where we struggle to understand what is real, from advanced CGI in movies to even faking the news.

Know how to adapt quantum computing and machine learning algorithms.

Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management.

Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook's open-source Prophet model, and Amazon's DeepAR model.

Dive into and apply practical machine learning and dataset categorization techniques while learning Tensorflow and deep learning.

Gain a thorough understanding of supervised learning algorithms by developing use cases with Python.

Develop machine learning models across various domains.

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

Learn the concepts of time series from traditional to bleeding-edge techniques.

Understand the current and future research into technologies that underpin the increasing capabilities of automation technologies and their impact on the working world of the future.

Understand and implement big data analysis solutions in pandas with an emphasis on performance.

Apply computer vision and machine learning concepts in developing business and industrial applications ?

Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries.

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

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice.

Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book.

Explore the adoption of chatbots in business by focusing on the design, deployment, and continuous improvement of chatbots in a business, with a single use-case from the banking and insurance sector.

Leverage machine and deep learning models to build applications on real-time data using PySpark.

Implement microservices starting with their architecture and moving on to their deployment, manageability, security, and monitoring.

Design, build, and justify an optimal Microsoft IoT footprint to meet your project needs.

Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies.

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.

This book shows you how to build real-time image processing systems all the way through to house automation.

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

This book is meant as a textbook for undergraduate and graduate students who are willing to understand essential elements of machine learning from both a theoretical and a practical perspective.

This book is the first comprehensive guide to the intersection of data governance and machine learning (ML) projects.

This book highlights a comprehensive overview of the recent advancements and challenges in the field of cybersecurity with a focus on the integration of artificial intelligence (AI), machine learning (ML), and blockchain technologies.

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

This book introduces a new type of data poisoning attack, dubbed, backdoor attack.

The past decade has witnessed extraordinary advances in artificial intelligence.

Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries.

Large language models (LLMs) have proven themselves to be powerful tools for solving a wide range of tasks, and enterprises have taken note.

In the digital age, our personal data has become a valuable commodity, sought after by corporations, organizations, and governments alike.

This book is your comprehensive guide to creating powerful, end-to-end deep learning workflows on Amazon Web Services (AWS).

The process of learning words and languages may seem like an instinctual trait, inherent to nearly all humans from a young age.

Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data.

As the size of multimedia databases grows, retrieval becomes a key challenge in multimedia database management.

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

Hybrid Artificial Intelligent Systems (HAIS) try to deal with the complexity of real world phenomena using a multidisciplinary approach and a plurality of techniques.

Learn how to maximize your use of, and benefit from AI, personally and professionally while staying safe.

This book focuses on cloud-based platforms, advanced programming, machine learning models and programming approaches to assess water and other natural resources, flood impact, land use land cover (LULC), global forest change, global forest canopy height and pantropical nation-level carbon stock, among other areas.

Unlock the world of data science—no coding required.

This book focuses on the prominent innovative business models and employability implications of artificial intelligence in the healthcare industry 4.

Professor Judea Pearl won the 2011 Turing Award "e;for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning.

This book introduces the concept of Event Mining for building explanatory models from analyses of correlated data.

This book introduces the concept of Event Mining for building explanatory models from analyses of correlated data.

Learn how to apply test-driven development (TDD) to machine-learning algorithmsand catch mistakes that could sink your analysis.

Learn how to apply test-driven development (TDD) to machine-learning algorithmsand catch mistakes that could sink your analysis.