Machine Learning Algorithms in Depth av Vadim Smolyakov

Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: * Monte Carlo Stock Price Simulation * Image Denoising using Mean-Field Variational Inference * EM algorithm for Hidden Markov Models * Imbalanced Learning, Active Learning and Ensemble Learning * Bayesian Optimisation for Hyperparameter Tuning * Dirichlet Process