Python Machine Learning (3rd Edition)

résumé

contents

Giving Computers the Ability to Learn from Data
Training Simple ML Algorithms for Classification
ML Classifiers Using scikit-learn
Building Good Training Datasets - Data Preprocessing
Compressing Data via Dimensionality Reduction
Best Practices for Model Evaluation and Hyperparameter Tuning
Combining Different Models for Ensemble Learning
Applying ML to Sentiment Analysis
Embedding a ML Model into a Web Application
Predicting Continuous Target Variables with Regression Analysis
Working with Unlabeled Data - Clustering Analysis
Implementing Multilayer Artificial Neural Networks
Parallelizing Neural Network Training with TensorFlow
TensorFlow Mechanics
Classifying Images with Deep Convolutional Neural Networks
Modeling Sequential Data Using Recurrent Neural Networks
GANs for Synthesizing New Data
RL for Decision Making in Complex Environments