Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách gia công, Bìa mềm
Year:2023
Language:english
Pages:456
This book is for machine learning engineers, data
scientists, and machine learning researchers looking to extend their
data science toolkit and explore causal machine learning. It will also
help developers familiar with causality who have worked in another
technology and want to switch to Python, and data scientists with a
history of working with traditional causality who want to learn causal
machine learning. It's also a must-read for tech-savvy entrepreneurs
looking to build a competitive edge for their products and go beyond the
limitations of traditional machine learning.
Table of Contents
Causality – Hey, We Have Machine Learning, So Why Even Bother?
Judea Pearl and the Ladder of Causation
Regression, Observations, and Interventions
Graphical Models
Forks, Chains, and Immoralities
Nodes, Edges, and Statistical (In)dependence
The Four-Step Process of Causal Inference
Causal Models – Assumptions and Challenges
Causal Inference and Machine Learning – from Matching to Meta-Learners
Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More
Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond
Can I Have a Causal Graph, Please?
Causal Discovery and Machine Learning – from Assumptions to Applications
Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond
Epilogue
