Gain the critical skills you need to manage data science teams, understand and evaluate statistical and machine learning models., and make decisions for your data science team and organization with confidence.
Key Features
Learn how to frame business problems as machine learning use-cases
Get a handle of on key machine learning and statistical concepts to better understand and evaluate data science results
Understand how to manage a data science team and structure a data science project from beginning to end
Book DescriptionWith the rise in the use of data science and artificial intelligence across industries, executives who may not have had a formal education and training in statistics and machine learning, are expected to make decisions around the outputs of complex models and manage data scientists and machine learning engineers. Leaders and managers within who work with, or are looking to bring data science into their departments and teams; can learn the fundamentals of data science to make better decisions. Decision makers reading this book will gain a solid understanding of the key concepts within statistics and machine learning to better and resource data science projects; interpret models developed by data scientists and machine learning engineers, and evaluate model accuracy, bias and fairness.
with step-by-step explanations of essential concepts and practical examples, you will begin by understanding fundamental concepts of statistical inference and machine learning. You will then learn how to interpret and evaluate machine learning models, and recent innovations in artificial intelligence.
By the end of this book, you will be able to better equipped to frame business challenges as data science problems, manage data science projects, and become an effective data leader within your organization.What you will learn
Understand the statistical foundations of data science, how to interpret common statistical quantities, and statistical decision making
Learn the core vocabulary of machine learning, and key techniques within supervised, unsupervised machine learning and reinforcement learning
Learn the basics of how to interpret and evaluate interpreting and evaluating statistical and machine learning models
Understand the data science lifecycle, and what to consider when planning the development, testing, deployment, and monitoring of models in production
Recognize in which circumstances statistical and machine learning modelling modeling is appropriate, and where other approaches, such as traditional business intelligence are more appropriate
Learn how to effectively manage data teams and data science projects to manage data teams and data science projects effectively
Who this book is forExecutives who want to learn how to understand and apply data science methods methods to make better decisions and those who work with or manage data scientists and machine learning engineers and need to understand how to interpret their models to make better business decisions and understand the accuracy and uncertainty around their statistical and machine learning models.
- ISBN10 1837637296
- ISBN13 9781837637294
- Publish Date 31 July 2024
- Publish Status Forthcoming
- Publish Country GB
- Imprint Packt Publishing Limited
- Format Paperback
- Language English