Introduction

This newsletter brings you a quick round-up from the data world, covering the latest developments to interesting researches. Stay engaged to find out.

Articles:

  • Top data books of 2021
    In today’s day and age, most of our learning is done through online learning via our electronic devices, whether it be online university classes, independent courses, or online tutorials. On the other hand, books are an amazing way of learning structurally and at one’s own pace. This article presents the top data books of 2021 which can come in handy in expanding your knowledge about ‘Data’.
  • Unlocking Human rights information with machine learning
    Finding the right information from piles of evolving precedents for Human Rights Defenders can be a strenuous task with everlasting manual work of skimming through the documents. The curation and cataloging of the documents make this process much easier, but even the cataloging process needs manual labor.  Here’s how HURIDOCS turned to ML to make this curation process 13 times faster.
  • Applications for ML Algorithms in Trading
    Machine Learning and AI have slowly made their way into every aspect of our life, and continue to do so. With the introduction of ML in trading, the conventional method is rapidly going extinct. Trading comes off in a highly competitive environment as traders have the constant pressure to make accurate decisions to maximize their profits. So, how is ML leveraging in the trading industry?
  • 5 Most In-Demand Machine Learning Projects That Will Get You Hired
    When looking for a job in the Machine Learning arena, academic projects contribute significantly to your resume especially if you are a fresher. This article gives you an idea about the 5 most in-demand ML projects that you can include in your portfolio which will boost your chance of landing that job.

News:

  • Do You Know How Your Teams Get Work Done?
    According to this report by Harvard Business Review, when managers were asked about their team’s work they did not know or could not remember 60% of it. This can lead to unrealistic digital transformation targets and poor allocation of resources.”The study showed that employing ML algorithms reduced the average work-recall gap from ~60% to 24%.”
  • Introducing TensorFlow Graph Neural Networks
    Tensorflow releases TensorFlow Graph Neural Networks (GNN), a library designed to work with graph-structured data. Graphs are all around us, a set of objects or people and the connection between them can be described by a graph. The recent advances in the capabilities of modern GNNs have led to advances in domains as varied as traffic prediction, rumor and fake news detection, etc.
  • Infuse your Tableau dashboard with real-world AI and machine learning from Aible
    Tableau and Aible is a way to mix Business Intelligence with Artificial Intelligence. Aible extension in Tableau lets you unlock the predictive abilities in your data with just a few clicks. It uncovers hidden patterns within the data resulting in faster and better analysis.

Tweets:

Conclusion

From most demanded ML projects to the newest application of ML to bridge the average work-recall gap, I hope some informative content was brought to your attention through this newsletter.

Have a Happy Weekend!