This book is meant as a textbook for undergraduate and graduate students who are willing to understand essential elements of machine learning from both a theoretical and a practical perspective. The choice of th
该教科书涵盖了一系列主题,包括最近邻、线性模型、决策树、集成学习、模型评估和选择、降维、组装各种学习阶段、聚类和深度学习,并介绍了用于数据科学和计算的基本 Python 包。机器学习,例如 NumPy、Pandas、Matplotlib、Scikit-Learn、XGBoost 和带有 TensorFlow 后端的 Keras。鉴于 Python 编程语言目前在机器学习中的主导...
当当中华商务进口图书旗舰店在线销售正版《海外直订Machine Learning With Python: An In-Depth Guide Beyond the Basics 使用Python的机器学习:超越基础的深入指南》。最新《海外直订Machine Learning With Python: An In-Depth Guide Beyond the Basics 使用Python的机器
Book description Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own...
A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models. Purchase of the print or Kindle book includes a free eBook in PDF format...
Machine Learning with PyTorch and Scikit-Learnhas been a long time in the making, and I am excited to finally get to talk about the release of my new book. Initially, this project started as the 4th edition ofPython Machine Learning. However, we made so many changes to the book that we...
Advanced Machine Learning with Python是John Hearty创作的计算机网络类小说,QQ阅读提供Advanced Machine Learning with Python部分章节免费在线阅读,此外还提供Advanced Machine Learning with Python全本在线阅读。
it'scrucialtoknowhowamachine"learns"underthehood.Thisbookwillguideyouthroughtheimplementationandnuancesofmanypopularsupervisedmachinelearningalgorithmswhilefacilitatingadeepunderstandingalongtheway.You’llembarkonthisjourneywithaquickoverviewandseehowsupervisedmachinelearningdiffersfromunsupervisedlearning.Next,weexplore...
Earlier in this book, we mentioned that one reason to learn Python is its popularity in the scientific computing and machine learning communities. This chapter introduces machine learning by demonstrating how to build a classifier from first principles. The lab section then introduces a set of ...
• Learn applied machine learning with a solid foundation in theory • Clear, intuitive explanations take you deep into the theory and practice of Python machine learning • Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book Descrip...