Mastering Machine Learning Algorithms: Expert Techniques for Implementing Popular Machine Learning Algorithms, Fine-tuning Your Models, and Understanding How They Work

• Editorial ‏ : ‎ Packt Publishing
• Fecha de publicación ‏ : ‎ 31 Enero 2020
• Edición ‏ : ‎ Segunda edición.
• Idioma ‏ : ‎ Inglés
• Número de páginas ‏ : ‎ 798 páginas
• ISBN-10 ‏ : ‎ 1838820299
• ISBN-13 ‏ : ‎ 978-1838820299

Mastering Machine Learning Algorithms de Giuseppe Bonaccorso se presenta como una obra técnica indispensable para quienes buscan profundizar en el diseño y la implementación de modelos de inteligencia artificial avanzados. Esta guía especializada aborda con rigor los algoritmos más representativos del aprendizaje automático, proporcionando a los técnicos y profesionales del sector informático las herramientas necesarias para dominar desde los fundamentos hasta las técnicas más innovadoras de optimización y ajuste fino de modelos. A través de sus páginas, el lector encontrará explicaciones detalladas sobre cómo funcionan internamente estas potentes herramientas matemáticas y cómo aplicarlas de manera eficiente en entornos reales de computación.

El contenido del libro está diseñado para que el lector aprenda a implementar algoritmos populares y a perfeccionar el rendimiento de sus sistemas mediante técnicas expertas de fine-tuning. Al ser un recurso orientado a un público técnico especializado, se enfoca en la resolución de problemas complejos y en el entendimiento profundo de la lógica algorítmica, permitiendo que los desarrolladores y científicos de datos eleven sus capacidades profesionales al siguiente nivel. Con un enfoque práctico y actualizado, esta obra de la editorial Packt Publishing Ltd se convierte en un aliado estratégico para cualquier experto que desee mantenerse a la vanguardia tecnológica en el campo del procesamiento de datos y la analítica predictiva.

Ideal para ingenieros y especialistas en software que buscan excelencia técnica, este manual garantiza una comprensión exhaustiva de los pilares que sostienen la inteligencia artificial moderna. Perfecciona tus habilidades técnicas y lleva tus modelos de aprendizaje automático a un nivel superior con esta guía experta de referencia global.

Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems

Key Features

• Updated to include new algorithms and techniques
• Code updated to Python 3.8 & TensorFlow 2.x
• New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications

Book Description

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains.

You will use all the modern libraries from the Python ecosystem - including NumPy and Keras - to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks.

By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.

What you will learn

• Understand the characteristics of a machine learning algorithm
• Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains
• Learn how regression works in time-series analysis and risk prediction
• Create, model, and train complex probabilistic models
• Cluster high-dimensional data and evaluate model accuracy
• Discover how artificial neural networks work - train, optimize, and validate them
• Work with autoencoders, Hebbian networks, and GANs

Who this book is for

This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.

Table of Contents

1. Machine Learning Model Fundamentals
2. Loss functions and Regularization
3. Introduction to Semi-Supervised Learning
4. Advanced Semi-Supervised Classifiation
5. Graph-based Semi-Supervised Learning
6. Clustering and Unsupervised Models
7. Advanced Clustering and Unsupervised Models
8. Clustering and Unsupervised Models for Marketing
9. Generalized Linear Models and Regression
10. Introduction to Time-Series Analysis
11. Bayesian Networks and Hidden Markov Models
12. The EM Algorithm
13. Component Analysis and Dimensionality Reduction
14. Hebbian Learning
15. Fundamentals of Ensemble Learning
16. Advanced Boosting Algorithms
17. Modeling Neural Networks
18. Optimizing Neural Networks
19. Deep Convolutional Networks
20. Recurrent Neural Networks
21. Auto-Encoders
22. Introduction to Generative Adversarial Networks
23. Deep Belief Networks
24. Introduction to Reinforcement Learning
25. Advanced Policy Estimation Algorithms