Mastering Machine Learning Algorithms Second Edition
Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work
Machine learning is a subset of artificial intelligence that aims to make modern-day computer systems more intelligent. The real power of machine learning lies in its algorithms, which make even the most difficult things capable of being handled by machines.
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 how to practically apply complex techniques like Maximum Likelihood Estimation, Hebbian Learning, 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.
Machine learning is a subset of artificial intelligence that aims to make modern-day computer systems more intelligent. The real power of machine learning lies in its algorithms, which make even the most difficult things capable of being handled by machines.
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 how to practically apply complex techniques like Maximum Likelihood Estimation, Hebbian Learning, 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.
