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In the field of social media data analytics, one popular area of research is the sentiment analysis of twitter data. Twitter is one of the most popular social media platforms in the world, with 330 million monthly active users and 500 million tweets sent each day. By carefully analyzing the sentiment of these tweets—whether they are positive, negative, or neutral, for example—we can learn a lot about how people feel about certain topics.
When handling time series data in your Data Science analysis work, a variety of common mistakes are made that are basic, but very important, to the processing of this type of data. Here, we review these issues and recommend the best practices.
How I cope with the boring days of deploying Machine Learning.
Review Key Concepts in Machine Learning on an Email Spam Dataset.
A practical deep dive into GPU Accelerated Python on cross-vendor graphics cards (AMD, Qualcomm, NVIDIA & friends) building machine learning algorithms using the Vulkan Kompute Python Framework.