Book description
NoneTable of contents
- Preface
- 1. The Machine Learning Pipeline
- 2. Fancy Tricks with Simple Numbers
- 3. Text Data: Flattening, Filtering, and Chunking
- 4. The Effects of Feature Scaling: From Bag-of-Words to Tf-Idf
- 5. Categorical Variables: Counting Eggs in the Age of Robotic Chickens
- 6. Dimensionality Reduction: Squashing the Data Pancake with PCA
- 7. Nonlinear Featurization via K-Means Model Stacking
- 8. Automating the Featurizer: Image Feature Extraction and Deep Learning
- 9. Back to the Feature: Building an Academic Paper Recommender
- A. Linear Modeling and Linear Algebra Basics
- Index
Product information
- Title: Feature Engineering for Machine Learning
- Author(s):
- Release date:
- Publisher(s):
- ISBN: None
You might also like
book
SQL for Data Analysis
With the explosion of data, computing power, and cloud data warehouses, SQL has become an even …
book
Reinforcement Learning
Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, …
book
Analytical Skills for AI and Data Science
While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, …
book
Designing Machine Learning Systems
Machine learning systems are both complex and unique. Complex because they consist of many different components …