Mastering Machine Learning Algorithms

Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition

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. Les mer
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528,-

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Paperback
Legg i
Paperback
Legg i
Vår pris: 528,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager

Om boka

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 DescriptionMastering 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 forThis 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.

Fakta

Innholdsfortegnelse

Table of Contents

Machine Learning Model Fundamentals
Loss functions and Regularization
Introduction to Semi-Supervised Learning
Advanced Semi-Supervised Classifiation
Graph-based Semi-Supervised Learning
Clustering and Unsupervised Models
Advanced Clustering and Unsupervised Models
Clustering and Unsupervised Models for Marketing
Generalized Linear Models and Regression
Introduction to Time-Series Analysis
Bayesian Networks and Hidden Markov Models
The EM Algorithm
Component Analysis and Dimensionality Reduction
Hebbian Learning
Fundamentals of Ensemble Learning
Advanced Boosting Algorithms
Modeling Neural Networks
Optimizing Neural Networks
Deep Convolutional Networks
Recurrent Neural Networks
Auto-Encoders
Introduction to Generative Adversarial Networks
Deep Belief Networks
Introduction to Reinforcement Learning
Advanced Policy Estimation Algorithms

Om forfatteren

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.