Machine Learning in Python. Essential Techniques for Predictive Analysis

Your no-nonsense guide to making sense of machine learning 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 learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

50 Techniques for Engaging Students and Assessing Learning in College Courses Do you want to: Know what and how well your students are learning? Promote active learning in ways that readily integrate assessment? Gather information that can help make grading more systematic and streamlined? Efficiently collect solid learning outcomes data for institutional assessment? Provide evidence of your teaching effectiveness for promotion and tenure review? Learning Assessment Techniques provides 50 easy-to-implement active learning techniques that gauge student learning across academic disciplines and learning environments. Using Fink's Taxonomy of Significant Learning as its organizational framework, it embeds assessment within active learning activities. Each technique features: purpose and use, key learning goals, step-by-step implementation, online adaptation, analysis and reporting, concrete examples in both on-site and online environments, and key references—all in an easy-to-follow format. The book includes an all-new Learning Goals Inventory, as well as more than 35 customizable assessment rubrics, to help teachers determine significant learning goals and appropriate techniques. Readers will also gain access to downloadable supplements, including a worksheet to guide teachers through the six steps of the Learning Assessment Techniques planning and implementation cycle. College teachers today are under increased pressure to teach effectively and provide evidence of what, and how well, students are learning. An invaluable asset for college teachers of any subject, Learning Assessment Techniques provides a practical framework for seamlessly integrating teaching, learning, and assessment.

A guide to thirty-five creative assignments for pairs and groups Collaborative Learning Techniques is the bestseller that college and university faculty around the world have used to help them make the most of small group learning. A mountain of evidence shows that students who learn in small groups together exhibit higher academic achievement, motivation, and satisfaction than those who don't. Collaborative learning puts into practice the major conclusion from learning theory: that students must be actively engaged in building their own minds. In this book, the authors synthesize the relevant research and theory to support thirty-five collaborative learning activities for use in both traditional and online classrooms. This second edition reflects the changed world of higher education. New technologies have opened up endless possibilities for college teaching, but it's not always easy to use these technologies effectively. Updated to address the challenges of today's new teaching environments, including online, «flipped,» and large lectures, Collaborative Learning Techniques is a wonderful reference for educators who want to make the most of any course environment. This revised and expanded edition includes: Additional techniques, with an all-new chapter on using games to provide exciting, current, technologically-sophisticated curricula A section on effective online implementation for each of the thirty-five techniques Significantly expanded pedagogical rationale and updates on the latest research showing how and why collaborative learning works Examples for implementing collaborative learning techniques in a variety of learning environments, including large lecture classes and «flipped» classes Expanded guidance on how to solve common problems associated with group work The authors guide instructors through all aspects of group work, providing a solid grounding in what to do, how to do it, and why it is important for student learning. The detailed procedures in Collaborative Learning Techniques will help teachers make sure group activities go smoothly, no matter the size or delivery method of their classes. With practical advice on how to form student groups, assign roles, build team spirit, address unexpected problems, and evaluate and grade student participation, this new edition of the international classic makes incorporating effective group work easy.

A how-to resource for incorporating social media into training Whether you work in a traditional or virtual classroom, social media can broaden your reach and increase the impact of training. In Social Media for Trainers, e-learning and new media expert Jane Bozarth provides an overview of popular tools, including blogs, wikis, Twitter, Facebook, YouTube, SlideShare, Flickr, and others. You'll learn to leverage each medium's unique features and applications to deliver training, facilitate discussions, and extend learning beyond the confines of a training event. This key resource offers a new set of powerful tools for augmenting and enhancing the value of your training. PRAISE FOR SOCIAL MEDIA FOR TRAINERS «Clear explanations and practical examples of the use of social media for learning, make this book essential reading for all workplace trainers.» —Jane Hart, founder, Centre for Learning and Performance Technologies, and founding member of the Internet Time Alliance «… a practical, intelligent book teaching trainers how to effectively utilize technology for real learning outcomes.» —Karl Kapp, professor of Instructional Technology at Bloomsburg University and author of Learning in 3D and Gadgets, Games and Gizmos for Learning «Trainers who want to succeed in the new social learning world should read this book. Jane has made social media easy, practical, and simple to use.» —Ray Jimenez, PhD, Chief Learning Architect, VignettesLearning.com

Use Big Data and technology to uncover real-world insights You don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast. With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. In no time, you'll learn how to incorporate algorithms through data models, identify similarities and relationships in your data, and predict the future through data classification. Along the way, you'll develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get you stakeholder buy-in. Big Data has taken the marketplace by storm, and companies are seeking qualified talent to quickly fill positions to analyze the massive amount of data that are being collected each day. If you want to get in on the action and either learn or deepen your understanding of how to use predictive analytics to find real relationships between what you know and what you want to know, everything you need is a page away! Offers common use cases to help you get started Covers details on modeling, k-means clustering, and more Includes information on structuring your data Provides tips on outlining business goals and approaches The future starts today with the help of Predictive Analytics For Dummies.