Implement machine learning in Python and R Teach machines to perform pattern-oriented tasks and data analysis Description Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine lea...
Implement machine learning in Python and R Use machine learning to accomplish practical tasks Machine learning made easy! It may sound a bit intimidating, but machine learning is an exciting new way to teach your computer to perform all sorts of important and useful tasks. This book is the eas...
For Dummies 出版 2016-05 装帧 平装 正版‧ 干净卫生 ‧ 当天发货 了解多抓鱼的环保探索 简介和目录 From the Back Cover Learn how machine learning algorithms are invaluable Implement machine learning in Python and R Use machine learning to accomplish practical tasks Machine learning made easy! It ma...
Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R sourc... Show more + Related Products About the Author John Mueller has produced hundreds of books and arti...
介绍:机器学习开源软件,收录了各种机器学习的各种编程语言学术与商业的开源软件.与此类似的还有很多例如:DMOZ - Computers: Artificial Intelligence: Machine Learning: Software, LIBSVM -- A Library for Support Vector Machines, Weka 3: Data Mining Software in Java, scikit-learn:Machine Learning in Python,...
《Brief History of Machine Learning》 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机、神经网络、决策树、SVM、Adaboost到随机森林、Deep Learning. 《Deep Learning in Neural Networks: An Over…
Machine Learning: A Probabilistic Perspectiveby Kevin P. Murphy Advanced Machine Learning with Pythonby John Hearty Reinforcement Learning: An Introductionby Richard S. Sutton and Andrew G. Barto Causal Inference in Statistics: A Primerby Judea Pearl, Madelyn Glymour and Nicholas P. Jewell ...
介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最新版本《神经网络与深度学习综述》本综述的特点是以时间排序,从1940年开始讲起,到60-80年代,80-90年代,一直讲到2000年后及最近几年的进展。涵盖了deep learning里各种tricks,引用非常全面. 《A Gentle Introduction to Scikit-Learn: A Python Machine Learning ...
The train_test_split() function in Python simplifies this process. from sklearn.model_selection import train_test_split X = dataset.drop(columns=['Survived']) y = dataset['Survived'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)</> ...
The Python version is forthcoming soon and can be pre-ordered onAmazon (US). What this book is not about This book deals with machine learning (ML) tools and their applications in factor investing. Factor investing is a subfield of a large discipline that encompasses asset allocation, quantitat...