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- Title: The Big Book of Machine Learning Use Cases
- Author(s) Databricks
- Publisher: Databricks
- Hardcover/Paperback: N/A
- eBook: PDF
- Language: English
- ISBN-10: N/A
- ISBN-13: N/A
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The world of machine learning is evolving so quickly that it's challenging to find real-world use cases that are relevant to what you're working on.
That's why we collected these technical blogs from industry thought leaders with practical use cases you can leverage today. This how-to reference guide provides everything you need - including code samples and notebooks - to start putting Machine Learning to work.
- Use dynamic time warping and MLflow to detect sales trends series
- Perform multivariate time series forecasting with recurrent neural networks
- Access new product capabilities with demos
- Detect financial fraud at scale with decision trees and other Machine Learning technologies.
- Databricks is the data and AI company.
- Machine Learning
- Deep Learning and Neural Networks
- Artificial Intelligence
- Data Science and Engineering
- Financial Engineering, Financial Technologies (FinTech)
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Machine Learning Engineering (Andriy Burkov)
The most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. It embraces the most important thing you need to know about machine learning: mistakes are possible.
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Machine Learning Yearning (Andrew Ng)
You will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.
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Interpretable Machine Learning: Black Box Models Explainable
This book explains to you how to make (supervised) machine learning models interpretable. The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and NLP tasks.
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Dive into Deep Learning (Aston Zhang, et al.)
This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.
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Python Machine Learning Projects (Brian Boucheron, et al)
This book tries to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning. If you know some Python and you want to use machine learning and deep learning, pick up this book.
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Machine Learning from Scratch (Danny Friedman)
This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox.
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The Hundred-Page Machine Learning Book (Andriy Burkov)
Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.
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Reinforcement Learning: An Introduction, Second Edition
It provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.
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