Practical Machine Learning Cookbook. Supervised and unsupervised machine learning simplified

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Practical Predictive Analytics. Analyse current and historical data to predict future trends using R, Spark, and more

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Practical Real-time Data Processing and Analytics. Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

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Practical Site Reliability Engineering. Automate the process of designing, developing, and delivering highly reliable apps and services with SRE

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Practical Threat Intelligence and Data-Driven Threat Hunting. A hands-on guide to threat hunting with the ATT&CK™ Framework and open source tools

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Practical Time Series Analysis. Master Time Series Data Processing, Visualization, and Modeling using Python

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Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition

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Principles of Data Science. Mathematical techniques and theory to succeed in data-driven industries

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