Until now, we have trained models on single instances, iterating an algorithm in order to minimize a target loss function. This approach is based on so-called strong learners, or methods that are optimized to solve a specific problem by looking for the best possible solution (highest accuracy). Another approach is based on a set of weak learners, which, formally, are estimators that are able to achieve an accuracy slightly higher than 0.5. In the real world, the actual estimators used in Ensemble Learning are much more accurate than their theoretical counterparts, but generally they are able to specialize a single region of the sample space and show bad performance while considering the whole dataset. Moreover, they can be trained in parallel or sequentially (with slight modifications to the parameters) and used as an ensemble (group) based on...
Machine Learning Algorithms
Machine Learning Algorithms
Overview of this book
Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight.
This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture.
By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Table of Contents (19 chapters)
Preface
Free Chapter
A Gentle Introduction to Machine Learning
Important Elements in Machine Learning
Feature Selection and Feature Engineering
Regression Algorithms
Linear Classification Algorithms
Naive Bayes and Discriminant Analysis
Support Vector Machines
Decision Trees and Ensemble Learning
Clustering Fundamentals
Advanced Clustering
Hierarchical Clustering
Introducing Recommendation Systems
Introducing Natural Language Processing
Topic Modeling and Sentiment Analysis in NLP
Introducing Neural Networks
Advanced Deep Learning Models
Creating a Machine Learning Architecture
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