Develop a mathematical intuition for how machine learning algorithms work so you can  improve model performance and effectively troubleshoot complex ML problems.

In Machine Learning Algorithms in Depth you’ll 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 Optimization for Hyperparameter Tuning
• Dirichlet Process K-Means for Clustering Applications
• Stock Clusters based on Inverse Covariance Estimation
• Energy Minimization using Simulated Annealing
• Image Search based on ResNet Convolutional Neural Network
• Anomaly Detection in Time-Series using Variational Autoencoders

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’ll learn the fundamentals of Bayesian inference and deep learning. You’ll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they’re put into action.