Kevin Jolly – författare

Archaeology doesn't just happen. With large numbers of people involved, the complex logistics of fieldwork, funding needed for projects of any size, and a bewildering set of legal regulations and ethical norms to follow, a well-run archaeological project requires careful and detailed planning. In this reader-friendly guide, Black and Jolly give novice researchers invaluable practical advice on the process of designing successful field projects. Encompassing both directed academic and directed CRM projects, they outline the elements needed in your professional toolkit, show step-by-step how an archaeological project proceeds, focus on developing appropriate research questions and theoretical models, and address implementation issues from NAGPRA regulations down to estimating the number of shovels to toss into the pickup. Sidebars explain important topics like the Section 106 process, the importance of ethnology and geology to archaeologists, OSHA requirements, and how to assess significance. Archaeology by Design is an ideal starting point for giving students and novices the big picture of a contemporary archaeological project.

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.Key FeaturesBuild your first machine learning model using scikit-learnTrain supervised and unsupervised models using popular techniques such as classification, regression and clusteringUnderstand how scikit-learn can be applied to different types of machine learning problemsBook DescriptionScikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.What you will learnLearn how to work with all scikit-learn s machine learning algorithmsInstall and set up scikit-learn to build your first machine learning modelEmploy Unsupervised Machine Learning Algorithms to cluster unlabelled data into groupsPerform classification and regression machine learningUse an effective pipeline to build a machine learning project from scratchWho this book is forThis book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.