Until now, we have discussed single methods that could be employed to solve specific problems. However, in real contexts, it's very unlikely to have well-defined datasets that can be immediately fed into a standard classifier or clustering algorithm. A machine learning engineer often has to design a full architecture that a layman would consider to be like a black box, where the raw data enters and the outcomes are automatically produced. All the steps necessary to achieve the final goal must be correctly organized and seamlessly joined together in a processing chain similar to a computational graph (indeed, it's very often a direct acyclic graph). Unfortunately, this is an unconventional process, as every real-life problem has its own peculiarities. However, there are some common steps that are normally included in almost any ML pipeline...
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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