Inhalt: Frameworks such as Apache Flink can help you build fast, scalable stream processing applications, but big data engineers still need to design smart use cases to achieve maximum efficiency. In this course, instructor Kumaran Ponnambalam demonstrates how to use Apache Flink and associated technologies to build stream-processing use cases leveraging popular patterns. Kumaran begins by highlighting the opportunities and challenges that stream processing brings to big data. He then goes over four popular patterns for stream processing: streaming analytics, alerts and thresholds, leaderboards, and real-time predictions. Along the way, he reviews example use cases and explains how to leverage Flink, as well as key technologies like MariaDB and Redis, to implement key examples. Umfang: 01:06:40
Inhalt: Text is a rich source of insights for businesses. Websites, social media, emails, and chats all contain valuable customer data. But to reap the rewards, you need to be able to analyze large amounts of unstructured text. Text mining is an essential skill for anyone working in big data and data science. This course teaches text-mining techniques to extract, cleanse, and process text using Python and the scikit-learn and nltk libraries. Kumaran Ponnambalam explains how to perform text analytics using popular techniques like word cloud and sentiment analysis. He then shows how to make predictions with text data using clustering, classification, and recommendations-otherwise known as predictive text. Along the way, he introduces important text analytics concepts such as lemmatization and n-grams. Umfang: 00:35:32.00
Inhalt: Social media, emails, blogs, and text messages offer businesses valuable insights into how their customers think and what they want. But mining this text data isn''t a straightforward process; rather, it requires a special set of tools and techniques. In this course, Kumaran Ponnambalam explores these tools and techniques, demonstrating how to use them to analyze text data in R and perform machine learning and predictions. Kumaran shows how to perform text analytics using popular methods like word cloud and sentiment analysis. He then shows how to make predictions with text data using clustering, classification, and recommendations-otherwise known as predictive text. Umfang: 00:40:44.00
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