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  • Machine Learning Zero-to-Hero: Everything you need in order to compete on Kaggle for the first time, step-by-step!

    I recently came across Rachel Tomas's article on the importance and value of writing about what you learn, and Julia Evans's advice on why and how to write, and thus I have decided to follow their advice and write an article (for the first time ever!).

    Source: TDS
    30726
  • How to write a production-level code in Data Science?

    Ability to write a production-level code is one of the sought-after skills for a data scientist role- either posted explicitly or not. For a software engineer turned data scientist this may not sound like a challenging task as they might have already perfected their skill at developing production level codes and deployed into production several times.

    Source: TDS
    12546
  • What Getting A Job In Data Science Might Look Like

    I've read a number of articles stating how hard it was to get into Analytics and Data Science. This hasn't been my experience, so I wanted to share. We'll look at interviewing, the tools I currently use, what parts of industry I wasn't prepared for in school, and what my career trajectory has looked like. But not in that particular order.

    Source: TDS
    13611
  • Choosing the Right Metric for Evaluating ML Models - Part 1

    In the world of postmodernism, Relativism has been, in its various guises, both one of the most popular and most reviled philosophical doctrines. According to Relativism, there is no universal and objective truth; rather each point of view has its own truth. You must be wondering why I am discussing it and how it is even related to Data Science.

    Source: TDS
    23217
  • AI-Based Operation: Learnings for Business and Technical Managers

    Using Reinforcement Learning to Tackle CitiBike Rebalancing Problems and Beyond

    Source: HOB
    10530
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