Thoughtful machine learning with Python a test-driven approach
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book fe...
| Other Authors: | |
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| Format: | eBook |
| Language: | Inglés |
| Published: |
Beijing :
O'Reilly
2017.
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| Edition: | First edition |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630069206719 |
Table of Contents:
- Probably approximately correct software
- A quick introduction to machine learning
- K-nearest neighbors
- Naive Bayesian classification
- Decision trees and random forests
- Hidden Markov models
- Support vector machines
- Neural networks
- Clustering
- Improving models and data extraction
- Putting it together: conclusion.
