Books in the Addison-Wesley Data & Analytics Series series

In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence. Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and most accessible way to get started with DRL. The authors teach through practical hands-on examples presented with their advanced OpenAI Lab framework. While providing a solid theoretical overview, they emphasize building intuition for the theory, rather than a deep mathematical treatment of results. Coverage includes: Components of an RL system, including environment and agents Value-based algorithms: SARSA, Q-learning and extensions, offline learning Policy-based algorithms: REINFORCE and extensions; comparisons with value-based techniques Combined methods: Actor-Critic and extensions; scalability through async methods Agent evaluation Advanced and experimental techniques, and more How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning Reduces the learning curve by relying on the authors' OpenAI Lab framework: requires less upfront theory, math, and programming expertise Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms Includes case studies, practical tips, definitions, and other aids to learning and mastery Prepares readers for exciting future advances in artificial general intelligenceThe accessible, hands-on, full-color tutorial for building practical deep reinforcement learning solutions How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning Reduces the learning curve by relying on the authors' OpenAI Lab framework: requires less upfront theory, math, and programming expertise Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms Includes case studies, practical tips, definitions, and other aids to learning and mastery Prepares readers for exciting future advances in artificial general intelligence

Data science and machine learningtwo of the world's hottest fieldsare attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help students with widely diverse backgrounds learn foundational Python so they can use it for data science and machine learning. This book is catered to introductory-level college courses on data science. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once students have learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and moreall created with Colab (Jupyter compatible) notebooks, so students can execute all coding examples interactively without installing or configuring any software.

Leading instructors Michael Freeman and Joel Ross guide readers through installing and configuring the tools needed to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you've uncovered. Step by step, students will master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything's focused on real-world application, so students can quickly start analyzing their own data and getting answers they can act upon.

The modules in this book will go beyond the dashboard to communicate business-relevant implications of data analyses using the analytic, visualization, and storytelling capabilities of Tableau, the most popular visualization software in use by businesses world today. Each chapter will split focus between discussing key components of design practice and data visualization and introducing a format for representing information with step-by-step guides for using Tableau. By the end of this book, readers will not only understand how data stories differ from traditional storytelling and how to purposefully craft a compelling data story, but also how to employ the horsepower of Tableau to structure data analysis projects so that they can effectively analyze, visualize, and communicate insights in a way that is meaningful for stakeholders across a variety of communication mediums.

The typical data science task in industry starts with an "ask” from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the context of the business's goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Written by two of the experts who've achieved breakthrough optimizations at BuzzFeed, it's packed with real-world examples that take you from start to finish: from ask to actionable insight. Andrew Kelleher and Adam Kelleher walk you through well-formed, concrete principles for approaching common data science problems, giving you an easy-to-use checklist for effective execution. Using their principles and techniques, you'll gain deeper understanding of your data, learn how to analyze noise and confounding variables so they don't compromise your analysis, and save weeks of iterative improvement by planning your projects more effectively upfront. Once you've mastered their principles, you'll put them to work in two realistic, beginning-to-end site optimization tasks. These extended examples come complete with reusable code examples and recommended open-source solutions designed for easy adaptation to your everyday challenges. They will be especially valuable for anyone seeking their first data science job -- and everyone who's found that job and wants to succeed in it.