Free To Learn
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Free to Learn
A leading expert in childhood development makes the case for why self-directed learning — "unschooling" — is the best way to get kids to learn. "All kids love learning. Most don't love school. That's a disconnect we've avoided discussing—until this lightning bolt of a book. If you've ever wondered why your curious kid is turning into a sullen slug at school, Peter Gray's Free to Learn has the answer. He also has the antidote." —Lenore Skenazy, author of Free-Range Kids In Free to Learn, developmental psychologist Peter Gray argues that in order to foster children who will thrive in today's constantly changing world, we must entrust them to steer their own learning and development. Drawing on evidence from anthropology, psychology, and history, he demonstrates that free play is the primary means by which children learn to control their lives, solve problems, get along with peers, and become emotionally resilient. A brave, counterintuitive proposal for freeing our children from the shackles of the curiosity-killing institution we call school, Free to Learn suggests that it's time to stop asking what's wrong with our children, and start asking what's wrong with the system. It shows how we can act—both as parents and as members of society—to improve children's lives and to promote their happiness and learning.
Learning How to Learn
A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course "Learning How to Learn" have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid "rut think" in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun.
An Introduction to Statistical Learning
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.