Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks (Villalobos Alva Jalil)(Paperback)

Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks (Villalobos Alva Jalil)(Paperback)

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book will introduce you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and

Data Mining and Machine Learning: Fundamental Concepts and Algorithms (Zaki Mohammed J.)(Pevná vazba)

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and

Driving Digital Transformation Through Data and AI: A Practical Guide to Delivering Data Science and Machine Learning Products (Borek Alexander)(Paperback)

Leading digital businesses such as Netflix, Amazon and Uber use data science and machine learning at scale in all of their core business processes, but many organizations struggle to expand their projects beyond a small pilot scope. This book enables all organizations to realize the promised value

Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python (Tatsat Hariom)(Paperback)

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies

Bayesian Data Analysis for the Behavioral and Neural Sciences: Non-Calculus Fundamentals (Hudson Todd E.)(Paperback)

This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced

Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry(Paperback / softback)

The Era of Artificial Intelligence and Machine Learning in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and

Model-Based Clustering and Classification for Data Science: With Applications in R (Bouveyron Charles)(Pevná vazba)

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified

Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics (Little Max A.)(Pevná vazba)

This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a

Introduction to Machine Learning with Python: A Guide for Data Scientists (Mller Andreas C.)(Paperback)

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning

Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning Library (Luu Hien)(Paperback)

Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work

Statistical Learning with Math and R: 100 Exercises for Building Logic (Suzuki Joe)(Paperback)

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building R programs. As the preliminary

Bayesian Data Analysis for the Behavioral and Neural Sciences: Non-Calculus Fundamentals (Hudson Todd E.)(Pevná vazba)

This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (Zheng Alice)(Paperback)

Practical Time Series Analysis: Prediction with Statistics and Machine Learning (Nielsen Aileen)(Paperback)

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis

Data Science Foundations Tools and Techniques: Core Skills for Quantitative Analysis with R and Git (Freeman Michael)(Paperback)

The Foundational Hands-On Skills You Need to Dive into Data Science Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills

Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning (Gutman Alex J.)(Paperback)

'Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful.'Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You've heard the hype around data--now get the facts. In Becoming a Data

Sparse Estimation with Math and R: 100 Exercises for Building Logic (Suzuki Joe)(Paperback)

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building R programs. Each chapter introduces the notion of

Data Science on AWS: Implementing End-To-End, Continuous AI and Machine Learning Pipelines (Fregly Chris)(Paperback)

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills

Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes (Panesar Arjun)(Paperback)

This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data.The book has been updated with the latest research in massive data,

Geographical Data Science and Spatial Data Analysis: An Introduction in R (Comber Lex)(Paperback)

We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial - it is collected some-where - and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges

Qualitative and Mixed Methods Data Analysis Using Dedoose: A Practical Approach for Research Across the Social Sciences (Salmona Michelle Suzanne)(Paperback)

Qualitative and Mixed Methods Data Analysis Using Dedoose(R) A Practical Approach for Research Across the Social Sciences by Michelle Salmona, Eli Lieber, Dan Kaczynski provides both new and experienced researchers with a guided introduction to dealing with the methodological complexity of mixed

Python for Data Analysis 3e: Data Wrangling with pandas, NumPy, and Jupyter - Wes McKinney

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively

Applied Regression Analysis and Generalized Linear Models (Fox John)(Pevná vazba)

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as

Behavioral Data Analysis with R and Python: Customer-Driven Data for Real Business Results (Buisson Florent)(Paperback)

Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as