Theory and implementation of state-of-the-art machine learning (ML) algorithms for real-world applications. Topics include supervised learning (applications in natural languages processing (NLP), sequence transcription, sentiment analysis, transformers, BERT), unsupervised learning (clustering, density estimation, dimensionality reduction, anomaly detection, and association rule learning), and reinforcement learning (RL).