First Published in 1998. Routledge is an imprint of Taylor & Francis, an informa company.

Students today are growing up in a digital world. These "digital natives" learn in new and different ways, so educators need new approaches to make learning both real and relevant for today's students. Marc Prensky, who first coined the terms "digital natives" and "digital immigrants," presents an intuitive yet highly innovative and field-tested partnership model that promotes 21st-century student learning through technology. Partnership pedagogy is a framework in which: - Digitally literate students specialize in content finding, analysis, and presentation via multiple media - Teachers specialize in guiding student learning, providing questions and context, designing instruction, and assessing quality - Administrators support, organize, and facilitate the process schoolwide - Technology becomes a tool that students use for learning essential skills and "getting things done" With numerous strategies, how-to's, partnering tips, and examples, Teaching Digital Natives is a visionary yet practical book for preparing students to live and work in today's globalized and digitalized world.

Summary Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand. About the Book Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems. What's Inside Predicting future behavior Performance evaluation and optimization Analyzing sentiment and making recommendations About the Reader No prior machine learning experience assumed. Readers should know Python. About the Authors Henrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning. Table of Contents PART 1: THE MACHINE-LEARNING WORKFLOW What is machine learning? Real-world data Modeling and prediction Model evaluation and optimization Basic feature engineering PART 2: PRACTICAL APPLICATION Example: NYC taxi data Advanced feature engineering Advanced NLP example: movie review sentiment Scaling machine-learning workflows Example: digital display advertising

Table of contents

Real-World Learning: Preparing for Your Profession Outside of the Classroom is designed to help students get the most out of experiential education opportunities. Through twelve engaging chapters, students explore topics such as lifelong learning, communication in the workplace, project management, work-life balance, professional ethics, and whether to pursue a career in academia or in the industry. Each chapter is designed around a specific career-related or career-development topic. The chapters provide students with a theoretical basis for the exploration of the topic, as well as opportunities for practical application through carefully crafted activities. The second edition features new chapters that discuss diversity and goalsetting, expanded content based on student feedback, and freshly reorganized chapters. All chapters feature clear learning objectives, real-life examples, personal reflection and development activities, and suggested readings. Real-World Learning is applicable to any student seeking an internship, cooperative education experience, service learning opportunity, or study abroad program. Bernadette Friedrich earned her Ph.D. in higher education administration at Michigan State University, where she is now the director of student advancement. Dr. Friedrich works with students and alumni at all levels to integrate their classroom knowledge with real-world work opportunities such as internships, cooperative education, and research experiences. A member of the American Society of Engineering Education, in Dr. Friedrich received the Alvah K. Borman Award in 2016 for her contributions to cooperative education in engineering.