Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.

This book guides you from the core mathematical foundations to Python implementations across a variety of applications, including finance, computer vision, and NLP. With a particular emphasis on probabilistic algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. Each algorithm is mathematically derived, followed by hands-on implementation with insightful code annotations and informative graphics.

Algorithms You’ll Explore

• 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

• Book's page
• Buy on Amazon
• GitHub repository