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

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

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 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

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 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

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

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

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

How to Speak How to Listen (Adler Mortimer J.)(Paperback)

Practical information for learning how to speak and listen more effectively. With over half a million copies in print of his 'living classic' How to Read a Book in print, intellectual, philosopher, and academic Mortimer J. Adler set out to write an accompanying volume on speaking and listening,

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (Brunton Steven L.)(Pevná vazba)

Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling and control of dynamical systems with modern methods in data science. It

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

An Introduction to Mathematical Statistics (Bijma Fetsje)(Paperback)

Statistics is the science that focuses on drawing conclusions from data, by modeling and analyzing the data using probabilistic models. In 'An Introduction to Mathematical Statistics' the authors describe key concepts from statistics and give a mathematical basis for important statistical methods

Minding the Machines: Building and Leading Data Science and Analytics Teams (Adamson Jeremy)(Paperback)

Learn and provide clear insights into how to structure and lead a successful analytics team. This is a deceptively challenging goal since there are no templates to work from. Establishing a project management office, information services, or human resources department is an understood process and

Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data (Emam Khaled El)(Paperback)

Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data-fake data generated from real data-so you can perform secondary

Malware Data Science: Attack Detection and Attribution (Saxe Joshua)(Paperback)

Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a 'big data' problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an

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

Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Good Decisions (Milton Michael)(Paperback)

Today, interpreting data is a critical decision-making factor for businesses and organizations. If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find

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

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,

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data - Hadley Wickham

Learn how to use R to turn data into insight, knowledge, and understanding. Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the tidyverse-a collection of R packages designed to work together to make data science fast,

Machine Learning, Revised and Updated Edition (Alpaydin Ethem)(Paperback)

A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we

Data Analytics with Hadoop: An Introduction for Data Scientists (Bengfort Benjamin)(Paperback)

Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you'll focus on particular

Practical Data Science: A Guide to Building the Technology Stack for Turning Data Lakes Into Business Assets (Vermeulen Andreas Franois)(Paperback)

Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets.The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data