Covers time series, principal component analysis, supervised and unsupervised learning for advanced analytics
Explains architectural patterns and anti-patterns in Python for scalable data solutions
Includes hands-on examples with PySpark, TensorFlow, and PyTorch for practical learning
Reviews recent databases like Neo4j, Elasticsearch, and MongoDB for modern data environments
Features chapters on clustering with neural networks, reinforcement learning, and recommender systems for real-world applications
Summarized by Shop
Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environme