Machine Learning With Python Introduction To A Guide For Data Scientists
User Manual:
Open the PDF directly: View PDF
Page Count: 340 [warning: Documents this large are best viewed by clicking the View PDF Link!]
- Cover
- Copyright
- Table of Contents
- Chapter 1. Introduction
- Chapter 2. Supervised Learning
- Classification and Regression
- Generalization, Overfitting and Underfitting
- Supervised Machine Learning Algorithms
- k-Nearest Neighbor
- Linear models
- Naive Bayes Classifiers
- Decision trees
- Ensembles of Decision Trees
- Kernelized Support Vector Machines
- Neural Networks (Deep Learning)
- Uncertainty estimates from classifiers
- Summary and Outlook
- Chapter 3. Unsupervised Learning and Preprocessing
- Chapter 4. Summary of scikit-learn methods and usage
- Chapter 5. Representing Data and Engineering Features
- Chapter 6. Model evaluation and improvement
- Chapter 7. Algorithm Chains and Pipelines
- Chapter 8. Working with Text Data