Kirk Borne
Recommended Books
Kirk Borne is the Principal Data Scientist and Executive Advisor at Booz Allen Hamilton in Virginia. Prior to that, Dr. Borne has been an astrophysicist, professor, and major influencer in the world of Big Data. At his talks, Dr. Borne frequently speaks out on ways to use different areas of data science to improve society, including AI and machine learning.
63 books on the list
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Machine Learning for Algorithmic Trading
Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition
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Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a researc...
Kirk Borne
Apr 10, 2021
Python Machine Learning By Example
Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition
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A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniquesKey FeaturesDive into machine learning algorithms to solve the complex challenges faced by data scientists todayExplore cutting edge content reflecting d...
Kirk Borne
Feb 16, 2021
The Design Thinking Playbook
Mindful Digital Transformation of Teams, Products, Services, Businesses and Ecosystems
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A radical shift in perspective to transform your organization to become more innovativeThe Design Thinking Playbook is an actionable guide to the future of business. By stepping back and questioning the current mindset, the faults of the status quo stand out in stark relief--and this guide gives you the tools and frameworks you need to kick off a d...
Kirk Borne
Feb 16, 2021
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact.This book introduces the key...
Kirk Borne
Feb 16, 2021
Artificial Intelligence with Python
Your complete guide to building intelligent apps using Python 3.x, 2nd Edition
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New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x and TensorFlow 2, with seven new chapters that cover RNNs, AI & Big Data, fundamental use cases, chatbots, and more....
Kirk Borne
Feb 16, 2021
AI Crash Course
A fun and hands-on introduction to machine learning, reinforcement learning, deep learning, and artificial intelligence with Python
Unlock the power of artificial intelligence with top Udemy AI instructor Hadelin de Ponteves. Key Features Learn from friendly, plain English explanations and practical activities Put ideas into action with 5 hands-on projects that show step-by-step how to build intelligent software Use AI to win classic video games and construct a virtual self-dri...
Kirk Borne
Feb 16, 2021
The Hundred-Page Machine Learning Book
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WARNING! To avoid buying counterfeit on Amazon, click on "See All Buying Options" and choose "Amazon.com" and not a third-party seller.Concise and to the point — the book can be read during a week. During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning th...
Kirk Borne
Feb 16, 2021
"The Book of R" is a comprehensive, beginner-friendly guide to R, the world's most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you'll find everything you need to begin using R effectively for statistical analysis.You'll start with th...
Kirk Borne
Feb 16, 2021
Machine Learning Design Patterns
Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
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The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hund...
Kirk Borne
Feb 03, 2021
Outlier Analysis
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With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in ma...
Kirk Borne
Nov 30, 2020
ApproachingAny Machine Learning Problem
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This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the a...
Kirk Borne
Nov 28, 2020Deep Learning Illustrated by Jon Krohn
User Story Mapping by Jeff Patton
Pre-Suasion by Robert Cialdini
Nudge by Richard H. Thaler
Guide to Competitive Programming by Antti Laaksonen
Building Analytics Teams by John K. Thompson
Building Machine Learning and Deep Learning Models on Google Cloud Platform by Ekaba Bisong
Python Crash Course by Eric Matthes
Programming PyTorch for Deep Learning by Ian Pointer
Blockchain in Healthcare by Vikram Dhillon
Generative Deep Learning by David Foster
Practical Data Analysis Using Jupyter Notebook by Marc Wintjen
The Fourth Age by Byron Reese
The Big Nine by Amy Webb
Mastering Python Networking by Eric Chou
Hands-On Deep Learning Algorithms with Python by Sudharsan Ravichandiran
An Introduction to 3D Computer Vision Techniques and Algorithms by Boguslaw Cyganek
Ensemble Methods in Data Mining by Giovanni Seni
Unblocked by Alison McCauley
Experimentation Works by Stefan H. Thomke
TensorFlow Machine Learning Cookbook by Nick McClure
Classification and Regression Trees by Leo Breiman
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques by Bart Baesens
Cyber Minds by Shira Rubinoff
Python Machine Learning by Sebastian Raschka
You Should Test That by Chris Goward
The Future Is Faster Than You Think by Peter H. Diamandis
Data Science and Business Intelligence by Heverton Anunciação
Deep Reinforcement Learning Hands-On by Maxim Lapan
Machine Learning by Kevin P. Murphy
Analytics Best Practices by Dr. Prashanth Southekal
Neural Smithing by Russell Reed
Industrial Applications of Machine Learning by Pedro Larrañaga
Building the Web of Things by Dominique Guinard
Platform Revolution by Geoffrey G. Parker
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
Mapping Experiences by James Kalbach
Building Intelligent Systems by Geoff Hulten
Practical Artificial Intelligence by Alan Pelz-Sharpe
Nuts About Data by Meor Amer
Machine Learning with R by Brett Lantz
The Practically Cheating Statistics Handbook by S. Deviant MAT
The Customer of the Future by Blake Morgan
More Is More by Blake Morgan
Data Science for Business by Foster Provost
Outside Insight by Jorn Lyseggen
Infinite Powers by Steven Strogatz
Ask, Measure, Learn by Lutz Finger
Designing Connected Products by Claire Rowland
The Golden Ratio by Mario Livio
Applied Predictive Modeling by Max Kuhn
Data Mining Techniques by Gordon S. Linoff