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Discover valuable machine learning techniques you can understand and apply using just high-school math.In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. About the book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data. What's inside Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets About the reader For readers who know basic Python. No machine learning knowledge necessary. About the author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple. Table of Contents 1 What is machine learning? It is common sense, except done by a computer 2 Types of machine learning 3 Drawing a line close to our points: Linear regression 4 Optimizing the training process: Underfitting, overfitting, testing, and regularization 5 Using lines to split our points: The perceptron algorithm 6 A continuous approach to splitting points: Logistic classifiers 7 How do you measure classification models? Accuracy and its friends 8 Using probability to its maximum: The naive Bayes model 9 Splitting data by asking questions: Decision trees 10 Combining building blocks to gain more power: Neural networks 11 Finding boundaries with style: Support vector machines and the kernel method 12 Combining models to maximize results: Ensemble learning 13 Putting it all in practice: A real-life example of data engineering and machine learning

The series Topics in Current Chemistry Collections presents critical reviews from the journal Topics in Current Chemistry organized in topical volumes. The scope of coverage is all areas of chemical science including the interfaces with related disciplines such as biology, medicine and materials science. The goal of each thematic volume is to give the non-specialist reader, whether in academia or industry, a comprehensive insight into an area where new research is emerging which is of interest to a larger scientific audience.Each review within the volume critically surveys one aspect of that topic and places it within the context of the volume as a whole. The most significant developments of the last 5 to 10 years are presented using selected examples to illustrate the principles discussed. The coverage is not intended to be an exhaustive summary of the field or include large quantities of data, but should rather be conceptual, concentrating onthe methodological thinking that will allow the non-specialist reader to understand the information presented. Contributions also offer an outlook on potential future developments in the field.The chapter'Lignin‑Based Composite Materials for Photocatalysis and Photovoltaics'is available open access under a CC BY 4.0 License via link.springer.com.

Ismael acaba de descubrir que tiene un abuelo lleno de manías, que el perro Tarzán es el mejor encontrando cosas y que los calamares a la romana los inventaron los jesuitas por aquello de darle un poco de gracia a la comida en tiempo de vigilia. Pero todavía tiene que averiguar qué encierra el maletero del coche y por qué su padre lleva años sin ir a ver a los abuelos. Menos mal que tiene un largo verano por delante.Daniel Nesquens fue Premio El Barco de Vapor 2011

Los cambios sociales y las necesidades de la ciudadanía plantean retos que la educación debe afrontar. Las preguntas que pretende responder este libro son las siguientes: ¿puede la educación responder a criterios de excelencia y equidad simultáneamente?, ¿cómo se puede construir una realidad más abierta y acorde a los espacios democráticos?, ¿se puede, desde la justicia social y curricular y la equidad educativa, responder a las necesidades de colectivos en riesgo de exclusión educativa y social?, ¿debe la escuela garantizar un proceso de socialización y educación libre de desigualdad de género?, ¿las instituciones que atienden a menores migrantes no acompañados se debaten entre el asistencialismo y la labor educativa?, ¿contribuye la formación profesional para el empleo en la integración de las personas en el mercado laboral?, ¿qué beneficios podría reportar la validación y acreditación de las cualificaciones profesionales para los colectivos más vulnerables?, ¿qué protagonismo debe tener la tecnología educativa en la búsqueda de la mejora educativa y el éxito académico?, ¿cuál es la importancia de la competencia digital para gestionar el proceso de aprendizaje dentro y fuera de las aulas?, ¿qué tipo de formación inicial del profesorado se está ofreciendo para atender a estudiantes con necesidades educativas espe-ciales? Y, finalmente, ¿se está aplicando en la formación inicial del profesorado tecnologías educativas que mejoren la didáctica en pro de un aprendizaje de calidad? Las respuestas a cada una de estas preguntas toman forma de capítulo en este libro. En esta obra se recogen interesantes aportaciones para la reflexión, el debate y su puesta en práctica.

Research on metal-containing liquid crystals is a rapidly expanding, multidisciplinary field with new materials continually being synthesized and novel applications being developed.'Metallomesogens'is the first comprehensive survey of the field, introducing the reader to: • materials design • synthesis • physical properties • emerging applications Carefully selected references round off this well-organized compendium. It is an indispensable guide to experienced researchers in coordination and organometallic chemistry as well as in liquid-crystal and materials science. Newcomers and graduate students will also benefit from this didactically sound introduction to the field.