Machine learning using R

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use i

Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This bo

Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of adva

Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniqu

Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This b

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapt

Build your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. You will then dive straight in

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonst

Discover how all levels Artificial Intelligence (AI) can be present in the most unimaginable scenarios of ordinary lives. This book explores subjects such as neural networks, agents, multi agent syste

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing

Migrate from pandas and scikit-Learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Na鴳e Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary to apply these methods using

Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related.

Apply analytics to business problems using two very popular software tools, SAS and R. No matter your industry, this book will provide you with the knowledge and insights you and your business partner

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosyste

Solve your AI and machine learning problems using complete and real-world code examples. Using a problem-solution approach, this book makes deep learning and machine learning accessible to everyday de

Implement real-time data processing applications on the Raspberry Pi. This book uniquely helps you work with data science concepts as part of real-time applications using the Raspberry Pi as a localized cloud. You'll start with a brief introduction to data science followed by a dedicated look at the fundamental concepts of Python programming. Here you'll install the software needed for Python programming on the Pi, and then review the various data types and modules available. The next steps are to set up your Pis for gathering real-time data and incorporate the basic operations of data science related to real-time applications. You'll then combine all these new skills to work with machine learning concepts that will enable your Raspberry Pi to learn from the data it gathers. Case studies round out the book to give you an idea of the range of domains where these concepts can be applied. By the end of Data Science with the Raspberry Pi, you'll understand that many applications are now de

Create applications using Industry 4.0. Discover how artificial intelligence (AI) and machine learning (ML) capabilities can be enhanced using the Internet of things (IoT) and secured using Blockchain

Learn how today’s businesses can transform themselves by leveraging real-time data and advanced machine learning analytics. This book provides prescriptive guidance for architects and develo