Cargando…
Mastering Machine Learning Algorithms : Expert techniques to implement popular machine learning algorithms and fine-tune your models.
This book is your guide to quickly get to grips with the most widely used machine learning algorithms. As a data science professional, this book will help you design and train better machine learning models to solve a variety of complex problems, and make the machine learn your requirements.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Machine Learning Model Fundamentals; Models and data; Zero-centering and whitening; Training and validation sets; Cross-validation; Features of a machine learning model; Capacity of a model; Vapnik-Chervonenkis capacity; Bias of an estimator; Underfitting; Variance of an estimator; Overfitting; The Cramér-Rao bound; Loss and cost functions; Examples of cost functions; Mean squared error; Huber cost function; Hinge cost function; Categorical cross-entropy; Regularization; Ridge; Lasso.
- ElasticNetEarly stopping; Summary; Chapter 2: Introduction to Semi-Supervised Learning; Semi-supervised scenario; Transductive learning; Inductive learning; Semi-supervised assumptions; Smoothness assumption; Cluster assumption; Manifold assumption; Generative Gaussian mixtures; Example of a generative Gaussian mixture; Weighted log-likelihood; Contrastive pessimistic likelihood estimation; Example of contrastive pessimistic likelihood estimation; Semi-supervised Support Vector Machines (S3VM); Example of S3VM; Transductive Support Vector Machines (TSVM); Example of TSVM; Summary.
- Chapter 3: Graph-Based Semi-Supervised LearningLabel propagation; Example of label propagation; Label propagation in Scikit-Learn; Label spreading; Example of label spreading; Label propagation based on Markov random walks; Example of label propagation based on Markov random walks; Manifold learning; Isomap; Example of Isomap; Locally linear embedding; Example of locally linear embedding; Laplacian Spectral Embedding; Example of Laplacian Spectral Embedding; t-SNE; Example of t-distributed stochastic neighbor embedding ; Summary; Chapter 4: Bayesian Networks and Hidden Markov Models.
- Conditional probabilities and Bayes' theoremBayesian networks; Sampling from a Bayesian network; Direct sampling; Example of direct sampling; A gentle introduction to Markov chains; Gibbs sampling; Metropolis-Hastings sampling; Example of Metropolis-Hastings sampling; Sampling example using PyMC3; Hidden Markov Models (HMMs); Forward-backward algorithm; Forward phase; Backward phase; HMM parameter estimation; Example of HMM training with hmmlearn; Viterbi algorithm; Finding the most likely hidden state sequence with hmmlearn; Summary; Chapter 5: EM Algorithm and Applications.
- MLE and MAP learningEM algorithm; An example of parameter estimation; Gaussian mixture; An example of Gaussian Mixtures using Scikit-Learn; Factor analysis; An example of factor analysis with Scikit-Learn; Principal Component Analysis; An example of PCA with Scikit-Learn; Independent component analysis; An example of FastICA with Scikit-Learn; Addendum to HMMs; Summary; Chapter 6: Hebbian Learning and Self-Organizing Maps; Hebb's rule; Analysis of the covariance rule; Example of covariance rule application; Weight vector stabilization and Oja's rule; Sanger's network.