Book Image

Mastering Machine Learning Algorithms

Book Image

Mastering Machine Learning Algorithms

Overview of this book

Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn v0.19.1. You will also learn how to use Keras and TensorFlow 1.x to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.
Table of Contents (17 chapters)
13
Deep Belief Networks

Machine Learning Model Fundamentals

Machine learning models are mathematical systems that share many common features. Even if, sometimes, they have been defined only from a theoretical viewpoint, research advancement allows us to apply several concepts to better understand the behavior of complex systems such as deep neural networks. In this chapter, we're going to introduce and discuss some fundamental elements that some skilled readers may already know, but that, at the same time, offer several possible interpretations and applications.

In particular, in this chapter we're discussing the main elements of:

  • Data-generating processes
  • Finite datasets
  • Training and test split strategies
  • Cross-validation
  • Capacity, bias, and variance of a model
  • Vapnik-Chervonenkis theory
  • Cramér-Rao bound
  • Underfitting and overfitting
  • Loss and cost functions
  • Regularization