Machine Learning, Data Science, Big Data, Analytics, AI
  1. The major advantage of focusing on AI-based methods is that they tackle each of the challenges faced by farmers from seed sowing to harvesting of crops separately and rather than generalising, provide customised solutions to a specific problem.
  2. Also: Getting Started with TensorFlow 2; An Introduction to Statistical Learning: The Free eBook; How Much Math do you need in Data Science?; Data Cleaning: The secret ingredient to the success of any Data Science Project
  3. Learn and appreciate the typical workflow for a data science project, including data preparation (extraction, cleaning, and understanding), analysis (modeling), reflection (finding new paths), and communication of the results to others.
  4. This is a central aspect of Data Science, which sometimes gets overlooked. The first step of anything you do should be to know your data: understand it, get familiar with it. This concept gets even more important as you increase your data volume: imagine trying to parse through thousands or millions of registers and make sense out of them.
  5. A step-by-step beginner’s guide to containerize and deploy ML pipeline serverless on AWS Fargate.

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