Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

A practical guide to implementing supervised and unsupervised machine learning algorithms in Python

Auteurs

• Tarek Amr

Paramètres

• Pages: 384 pages
• Temps de lecture: 14 heures

• Informatique, programmation

En savoir plus sur le livre

Explore the integration of scikit-learn with essential tools like NumPy, pandas, imbalanced-learn, and scikit-surprise to effectively tackle real-world machine learning challenges. This book provides practical insights and techniques, guiding readers through the application of these libraries to enhance their machine learning projects and improve data analysis skills. Ideal for those looking to deepen their understanding of machine learning implementations, it emphasizes hands-on problem-solving and real-world applications.

Achat du livre

• Année de publication: 2020
• Reliure: (souple)