Mejores libros de Aprendizaje Automático

This book explores the fascinating world of deep learning, which teaches computers to understand the world through a hierarchy of concepts. It covers mathematical and conceptual background, techniques used in industry, and research perspectives. Readers will learn about relevant topics in linear algebra, probability theory, and more, as well as practical applications in areas like natural language processing and computer vision. Perfect for students or engineers looking to incorporate deep learning into their work. Supplementary material available on the website.

A series of Deep Learning breakthroughs have boosted the whole field of machine learning over the last decade. Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you. This hands-on book shows you how to use: Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry point TensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networks Practical code examples that you can apply without learning excessive machine learning theory or algorithm details

Peter Norvig, Director de Investigación en Google, coautor de AIMA, uno de los libros de texto en IA más utilizados en el mundo: "Burkov ha acometido la muy util pero casi imposible y durísima tarea de reducir todo el aprendizaje automático a 100 páginas. Su éxito total al escoger los temas -tanto teóricos como prácticos- será de gran utilidad para los profesionales, y para el lector que es consciente de que están son las primeras 100 (en realidad 150 páginas) que leerá del tema, pero que no serán las últimas, proporciona una sólida introducción a la materia." Aurélien Géron, Ingeniero Senior en IA, autor del bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "La amplitud de los temas que el libro cubre en solo 100 páginas (¡algunas más como extra!) es asombrosa. Burkov no duda en entrar en las fórmulas matemáticas, algo que en los libros cortos se suele evitar. Realmente me ha gustado como el autor explica los conceptos fundamentales en solo unas pocas palabras. El libro puede ser muy útil tanto a los principiantes en el campo, como a los más experimentados, que sin duda hallarán valor en una visión tan amplia del campo." Karolis Urbonas, Jefe de Ciencia de Datos en Amazon: "Una gran introducción al aprendizaje automático por parte de un profesional de clase mundial." Chao Han, VP, Jefe de I&D en Lucidworks: "Ojalá hubiera tenido un libro así cuando era un estudiante de estadística tratando de aprender sobre aprendizaje automático." Sujeet Varakhedi, Jefe de Ingeniería en eBay: "El libro de Andriy hace un trabajo fantástico explicando los conceptos de manera concisa y a toda velocidad desde la primera página.'' Deepak Agarwal, VP de Inteligencia Artificial en LinkedIn: "Un libro maravilloso para los ingenieros que deseen incorporar el aprendizaje automático en su trabajo diario sin tener que gastar una cantidad enorme de tiempo.'' Vincent Pollet, Jefe de Investigación en Nuance: "Excelente lectura para comenzar con el aprendizaje automático.'' Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "Este es un manual compacto de "cómo hacer ciencia de datos" y predigo que se convertirá en un recurso para académicos y profesionales por igual. Con poco más de 100 páginas, el libro es lo suficientemente corto como para leerlo de una sola vez. Sin embargo, a pesar de su extensión, cubre todos los enfoques principales de aprendizaje automático, que van desde la regresión lineal y la regresión logística, hasta las modernas máquinas de vectores de soporte, aprendizaje profundo, gradient boosting y bosques aleatorios. Tampoco faltan detalles sobre los diversos enfoques y el lector interesado puede obtener más información sobre cualquier método en particular a través de la innovadora wiki complementaria.

"Discover the cutting-edge world of pattern recognition and machine learning with this comprehensive textbook. Bayesian methods and graphical models have transformed these fields in the past decade, and this book explores them while providing a thorough introduction to the subject matter. Perfect for advanced students, researchers, and practitioners, this book assumes no prior knowledge of the concepts and includes a self-contained introduction to basic probability theory. Get ready to dive into the exciting world of pattern recognition and machine learning!"

Learn the practical components and statistical concepts of machine learning with this clear and concise guide for absolute beginners. Written without the need for programming experience, you'll be introduced to core algorithms and visual examples to guide you through creating your first machine learning model using Python. The second edition includes new topics such as data scrubbing and ensemble modeling, making it an excellent starting point for those ready to step into the world of machine learning. This book is not a sequel and is a revamped version of the first edition, but with additional information.

Learn practical ways to build your own machine learning solutions using Python with this book! Perfect for beginners, authors Andreas Müller and Sarah Guido will guide you through the process of creating successful machine-learning applications with the scikit-learn library. You'll discover how to use machine learning algorithms and focus on the practical aspects of the process. Don't wait to unlock the full potential of machine learning with Python!

This comprehensive textbook introduces readers to machine learning and how it can be used to automatically detect patterns in data and predict future data. The author uses a unified, probabilistic approach and covers important background topics such as probability, optimization, and linear algebra. Recent developments in the field are discussed, and the book is filled with color images and examples from biology, text processing, computer vision, and robotics. The book stresses a model-based approach and includes pseudo-code for important algorithms. Suitable for upper-level undergraduates and beginning graduate students.

El aprendizaje automático ha progresado de manera notable en los últimos años. Hemos pasado del discurso casi inutilizable y el reconocimiento de imágenes a una precisión casi humana. Hemos pasado de máquinas que no podían ganar a un jugador de go decente a derrotar al campeón del mundo. Tras este progreso se encuentra el deep learning, una combinación de avances en ingeniería, prácticas adecuadas y teoría que permite crear una gran abundancia de aplicaciones inteligentes que antes eran imposibles. Deep Learning con Python presenta el campo del deep learning utilizando el lenguaje Python y la potente biblioteca Keras. Escrito por François Chollet, creador de Keras e investigador de Google AI, este libro desarrolla su comprensión mediante explicaciones intuitivas y ejemplos prácticos. Explorará conceptos complicados y practicará con aplicaciones en visión por ordenador, procesamiento de lenguaje natural y modelos generativos. Para cuando acabe, tendrá el conocimiento y las habilidades prácticas para aplicar el deep learning a sus propios proyectos.

Discover the world of data mining and machine learning with this comprehensive guide. Written by three prominent professors of statistics, this book provides a common conceptual framework for understanding the tools and ideas in various fields such as medicine, biology, finance, and marketing. With a focus on concepts rather than mathematics, it covers a broad range of topics including neural networks, support vector machines, classification trees, and boosting. With many examples and color graphics, this is a valuable resource for statisticians and anyone interested in data mining in science or industry.

Link to the GitHub Repository containing the code examples and additional material: https://github.com/rasbt/python-machi... Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible. Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively. This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results. You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world