Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This book’s practical case-studies reveal feature engineering techniques that upgrade your data wrangling—and your ML results.
In Feature Engineering Bookcamp you will learn how to:
Identify and implement feature transformations for your data
Build powerful machine learning pipelines with unstructured data like text and images
Quantify and minimize bias in machine learning pipelines at the data level
Use feature stores to build real-time feature engineering pipelines
Enhance existing machine learning pipelines by manipulating the input data
Use state of the start deep learning models to extract hidden patterns in data
Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This instantly-useful book skips the abstract mathematical theory and minutely-detailed formulas; instead you’ll learn through interesting code-driven case studies, including tweet classification, COVID detection, recidivism prediction, stock price movement detection, and more.
about the technology
Feature engineering is the secret weapon for improving your machine learning’s output. By enhancing the data ingestion, manipulation, and transformation elements of your pipeline, you can see dramatic improvements in your downstream results without endlessly fine-tuning parameters or chasing the latest models.
about the book
Feature Engineering Bookcamp delivers hands-on experience with important techniques for optimizing your training data. As you practice your skills in cleaning and transforming data, working with unstructured image and text data, and implementing bias mitigation, you’ll quickly see improvements in your end results. You’ll learn by exploring real-world scenarios from different domains, including healthcare, finance, and natural language processing.
about the reader
For experienced machine learning engineers familiar with Python.
about the author
Sinan Ozdemir is the director of data science at Directly, managing the AI and machine learning models that power the company’s intelligent customer support platform. He is a former lecturer of data science at Johns Hopkins University and the author of multiple textbooks on data science and machine learning. He holds a master’s degree in pure mathematics from Johns Hopkins University and is based in San Francisco, CA.
customers also reading
FREE domestic shipping on orders of three or more print books