Data Preparation for Machine learning 101: Why it’s important and how to do it — KDnuggets

  • Master coding, particularly python that is most ideal for machine learning. In addition to coding expertise, you should have good analytical and statistical skills.
  • For starters, you can begin by cloning codes from git repositories or tutorials. But, to become a sound ML/AI engineer, you must know and own what you’re doing.
  • Do not invent a solution and hunt for the problem. Instead, identify the problems and challenges to invent an automated solution.
  • Image classification/annotation of videos and images includes annotation of images, its description, bounding box definition, and more.
  • Conversational tagging A typical example would be chatbots wherein the data is labeled and trained to make conversations with users more realistic and relevant.
  • Sentiment analysis Labeling of data be it text or images to understand the sentiment of the content like in the case of tweet.
    Speech and text NLP is the labeling for audio and text sources.
  • Face detection Label image sets and train for accurate detection and prediction
  • Where to begin?



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Nandhini TS

Nandhini TS

Product Marketer | Content Creator | Creator of The Digitaldyno. Sticky notes and bulky planners make me happy!