Data Preparation for Machine learning : Why it’s important and how to do it
Coding is a prerequisite for successful business models.
I recently stumbled upon this answer by Alexandre Robicquet on Quora which was also published on Forbes. For a beginner who’s looking for the most sensible advice on
“How can I start learning about Machine Learning and Artificial Intelligence”, here’s what he says:
A condensed version of his perspective:
- 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.
(You may read his answer here).
But, that’s not it.
If you’re a coding nerd, double-check the quality and structure of data you feed into your algorithms as they play a huge role in inventing a successful analytical model.
There are 3 dimensions to building a successful AI/ML model:
Algorithms, data, and computation.
Creating accurate algorithms and applying computational skills is a part of the whole process, but, what is foundational to this?
Lay the groundwork with the right data.
To continue reading, jump ahead to Kdnuggets where this article was originally published.