Valuable lessons: How To Develop a Machine Learning Model From Scratch

This is the one of the most important content I was searching to read. To build a Machine Learning model is vital, unless if you are passion about Machine Learning. I started thinking what is needed to write. The process, we had seen in the pictures.

Before looking at the abstract of this content, we should know the simpler meaning of the Machine Learning. “Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves”.

The article from towardsdatascience website, shows you the step by step to build the model. I’m gonna say every sub-headings of the step and rest of the diagrams and formulae, please check the links below.

The following steps covers;

  • Define adequately our problem (objective, desired outputs…).
  • Gather data.
  • Choose a measure of success.
  • Set an evaluation protocol and the different protocols available.
  • Prepare the data (dealing with missing values, with categorial values…).
  • Spilit correctly the data.
  • Differentiate between over and underfitting, defining what they are and explaining the best ways to avoid them.
  • An overview of how a model learns.
  • What is regularization and when is appropiate to use it.
  • Develop a benchmark model.
  • Choose an adequate model and tune it to get the best performance possible.

This is overall steps to build a Machine Learning model from the scratch. I’m gonna paste the source link down below. I sincerely encourage you all visit further.


With respect.

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