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Julia for Machine Learning
Unleash the power of Julia for your machine learning tasks. We reveal why Julia is chosen for more and more data science and machine learning projects, including Julia's ability to run algorithms at lightning speed. Next, we show you how to set up Julia and various IDEs such as Jupyter. Afterward, we explore key Julia libraries, which are useful for data science work, including packages related to visuals, data structures, and mathematical processes. After building a foundation in Julia, we dive into machine learning, with foundational concepts reinforced by Julia use cases. The use cases build upon each other, reaching the level where we code a machine learning model from scratch using Juli...
Data Scientist Bedside Manner
Embrace the holistic set of skills and experiences required for data science success. (HINT: It's much more than just knowing math!) Know what it takes to become a star data scientist, and how data science compares with and leverages other disciplines such as artificial intelligence (AI). Explore how data science adds value by focusing on business questions and how to graduate from being a good technical professional to becoming an invaluable member of a business team. For those of us who are not data scientists, learn how to best leverage data science skills within your organization, how to hire a data scientist, and how to evaluate the outcome of a data science project. The approach provided in this book is supported by the rich experiences of the authors, combined with findings from interviews with top data science professionals.
Data Scientist
Learn what a data scientist is and how to become one. As our society transforms into a data-driven one, the role of the Data Scientist is becoming more and more important. If you want to be on the leading edge of what is sure to become a major profession in the not-too-distant future, this book can show you how. Each chapter is filled with practical information that will help you reap the fruits of big data and become a successful Data Scientist: Learn what big data is and how it differs from traditional data through its main characteristics: volume, variety, velocity, and veracity. Explore the different types of Data Scientists and the skillset each one has. Dig into what the role of the Da...
Julia for Data Science
After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialized script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: An overview of the data science pipeline along with an ...
The Data Path Less Traveled
Become proficient in using heuristics within the data science pipeline to produce higher quality results in less time. Although data professionals have used heuristics for many years within optimization-related applications, heuristics have been a vibrant area of research in various data-related areas, from machine learning to image processing. Heuristics also play a role in niche applications such as cybersecurity. In addition, the advent of AI and other data-driven methodologies have brought heuristics to the forefront of data-related work. In this book, we explore heuristics from a practical perspective. We illustrate how heuristics can help you solve challenging problems through simple e...
Data Science
Master the concepts and strategies underlying success and progress in data science. From the author of the bestsellers, Data Scientist and Julia for Data Science, this book covers four foundational areas of data science. The first area is the data science pipeline including methodologies and the data scientist's toolbox. The second are essential practices needed in understanding the data including questions and hypotheses. The third are pitfalls to avoid in the data science process. The fourth is an awareness of future trends and how modern technologies like Artificial Intelligence (AI) fit into the data science framework. The following chapters cover these four foundational areas: Chapter 1...
Data Science and Machine Learning Series
Learn all about time-series analysis, which can be leveraged within many industries including economics and artificial intelligence (AI). Explore the different kinds of time-series analysis, including stats-based, machine learning-based, and AI-based. Understand the most common evaluation metrics used, and the evaluation packages that are available within Python and Julia. Here is a link to all of Zacharias Voulgaris' machine learning, data science, and artificial intelligence (AI) videos.
AI for Data Science
Master the approaches and principles of Artificial Intelligence (AI) algorithms, and apply them to Data Science projects with Python and Julia code. Aspiring and practicing Data Science and AI professionals, along with Python and Julia programmers, will practice numerous AI algorithms and develop a more holistic understanding of the field of AI, and will learn when to use each framework to tackle projects in our increasingly complex world. The first two chapters introduce the field, with Chapter 1 surveying Deep Learning models and Chapter 2 providing an overview of algorithms beyond Deep Learning, including Optimization, Fuzzy Logic, and Artificial Creativity. The next chapters focus on AI ...
Data Science and Machine Learning Series
With all of the available data science material available to us, how do we determine which subset is truly valuable? This video will teach you how to find the diamonds in the data science haystack. Learn how to evaluate data science material. We will cover the criteria used to determine what constitutes quality data science material, share the various types of material available (books, videos, tutorials, and mentors), distinguish required from optional resources, and contrast data science education with data science training. Here is a link to all of Zacharias Voulgaris' machine learning, data science, and artificial intelligence (AI) videos.
A Pythonista's Guide to the Julia Language for Data Science
"This video provides a brief introduction to using Julia for data science. The presenter of this video, Zacharias Voulgaris, is author of these two books which are both available on Safari: 'Data Scientist' and 'Julia for Data Science'. Zack explains the benefits of Julia including most importantly the simplicity of coding without the sacrificing of performance. Learn how Julia compares to other languages including Python, Fortran, and C. Learn about the packages available in Julia and the pre-requisites to quickly picking up the language, including the importance of at least some knowledge of object-oriented (OO) paradigms. Get a basic understanding of REPL along with Julia commands and common types. We touch on IDEs and note the best one for data science applications (Jupyter). We briefly explain Stats in Julia including StatsBase, MultivariateStats, and StatsFuns. We discuss machine learning and plots along with their packages. We conclude with an overview of resources to learn more about the language."--Resource description page.
