Machine Learning And Statistical

This book bridges the gap between advances in the communities of computer science and physics–namely machine learning and statistical physics. It contains diverse but relevant topics in statistical physics, complex systems, network theory, and machine learning. Examples of such topics are: predicting missing links, higher-order generative modeling of networks, inferring network structure by tracking the evolution and dynamics of digital traces, recommender systems, and diffusion processes.

In the early 1990s, the establishment of the Internet brought forth a revolutionary viewpoint of information storage, distribution, and processing: the World Wide Web is becoming an enormous and expanding distributed digital library.

Learn how to use machine learning algorithms and statistical modeling for clustering, decision trees, etc by using R

Master Advanced Statistics, Deep Learning Optimization, Time Series Forecasting, Bayesian Modeling