Being smart helps. Working hard helps more. But working effectively helps most of all. There's a great deal that research can now tell us about how to study effectively. For 20 years, I've been reporting on research into memory and learning, and explaining how these findings can be translated into practical advice. In earlier books, I have presented a lot of detail on strategies in specific areas: note-taking, mnemonics, practice and revision. In this guide, I attempt to provide an overview of how to approach your learning, and the strategies you should consider using. The book covers: * preparing for learning * approaching a text * reading * taking notes * building understanding * navigating non-linear environments * dealing with lectures * memorizing * revising * building expertise in skills * how specific subjects differ in their needs & demands. The book can be used as an adjunct and quick reference for those who have the more in-depth workbooks, or as a simplified guide for those who want the bottom-line without the detail. This is not a book for students who want a magic bullet, who want a five-minute 'answer' to effortless study! But students who want to know that their time and effort are being used wisely, that their diligence will be rewarded with better marks and more long-lasting learning, this guide may be the answer they've long been looking for. Keywords: best study strategies for college students, textbook reading strategies for college, study skills, college study, successful learning

Implement scikit-learn into every step of the data science pipeline About This Book Use Python and scikit-learn to create intelligent applications Discover how to apply algorithms in a variety of situations to tackle common and not-so common challenges in the machine learning domain A practical, example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learn Who This Book Is For If you are a programmer and want to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this is the course for you. No previous experience with machine-learning algorithms is required. What You Will Learn Review fundamental concepts including supervised and unsupervised experiences, common tasks, and performance metrics Classify objects (from documents to human faces and flower species) based on some of their features, using a variety of methods from Support Vector Machines to Naive Bayes Use Decision Trees to explain the main causes of certain phenomena such as passenger survival on the Titanic Evaluate the performance of machine learning systems in common tasks Master algorithms of various levels of complexity and learn how to analyze data at the same time Learn just enough math to think about the connections between various algorithms Customize machine learning algorithms to fit your problem, and learn how to modify them when the situation calls for it Incorporate other packages from the Python ecosystem to munge and visualize your dataset Improve the way you build your models using parallelization techniques In Detail Machine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, scikit-learn is the unequivocal choice for machine learning. The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. You will learn to incorporate machine learning in your applications. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. By the end of this course you will have learned how to build applications that learn from experience, by applying the main concepts and techniques of machine learning. Style and Approach Implement scikit-learn using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of scikit-learn.

thimblesofplenty is a group of friends who also happen to be business people and avid readers. We wanted to keep up with the latest business books but found that time was a factor. So we divided out the work and each of us took a book and summarised it for the others. We though it might be a great idea to share these summaries with you. For a small price and a 3 minute time investment, our summary gives you some of the wisdom from the book, some food for thought and hopefully the impetus to make some time to read the whole book!

Learning Analytics Explained draws extensively from case studies and interviews with experts in order to discuss emerging applications of the new field of learning analytics. Educational institutions increasingly collect data on students and their learning experiences, a practice that helps enhance courses, identify learners who require support, and provide a more personalized learning experience. There is, however, a corresponding need for guidance on how to carry out institutional projects, intervene effectively with students, and assess legal and ethical issues. This book provides that guidance while also covering the evolving technical architectures, standards, and products within the field.

Written for busy teachers, trainers, managers and students, this ′dip-in, dip-out′ guide makes understanding theories of learning accessible and practical.

Metacognition plays an important role in numerous aspects of higher educational learning strategies. When properly integrated in the educational system, schools are better equipped to build more efficient and successful learning strategies for students in higher education. Metacognition and Successful Learning Strategies in Higher Education is a detailed resource of scholarly perspectives that discusses current trends in learning assessments. Featuring extensive coverage on topics such as spiritual intelligence strategies, literacy development, and ubiquitous learning, this is an ideal reference source for academicians, graduate students, practitioners, and researchers who want to improve their learning strategies using metacognition studies.

The must-read summary of Brooke Broadbent's book: "ABCs of e-Learning: Reaping the Benefits and Avoiding the Pitfalls". This complete summary of the ideas from Brooke Broadbent's book "ABCs of e-Learning" shows that with computers and the Internet being so widely accessible, the digital space is a perfect vehicle for cost-effective learning. Electronic learning, or e-Learning as it is known, has not been half as successful as it could be. Apart from its wide reach and cost effectiveness, it has many other benefits including its adaptable structure, its interactive nature, its flexibility, and most interestingly the fact that it is non-linear meaning participants can skip material or specify the order in which they learn, something that cannot be tailored in the classroom. Thanks to all these advantages, e-Learning is becoming increasingly popular at all levels of education. This summary helps to dissect the different forms of e-Learning and explains how to plan and roll out successful e-Learning courses with solid, measurable results. Added-value of this summary: • Save time • Understand key concepts • Increase your business knowledge To learn more, read "ABCs of e-Learning" and enhance the bottom line by making an organization more productive, saving money, and providing learners with more access to more training.

Get the Summary of Blake Boles's The Art of Self-Directed Learning in 20 minutes. Please note: This is a summary & not the original book. "The Art of Self-Directed Learning" by Blake Boles is a comprehensive guide to taking control of one's educational journey. It showcases the story of Laura Dekker, the youngest person to solo circumnavigate the globe, as an example of self-directed learning. The book emphasizes that self-directed learners are proactive in their education, choosing paths that align with their goals, whether within traditional schooling or through alternative methods...