day-10 of 180 | ML lifecycle | ML challenge| #mlwithakansha #ytshorts
In this Hindi-language YouTube short, Akansha Yadav outlines the machine learning (ML) lifecycle as a seven‑step process aimed at solving a problem. She begins by stating that the ultimate goal of the ML lifecycle is to provide a solution to a given problem. The steps she describes are: (1) Data gathering – collecting raw data and consolidating it into a single location (e.g., a Python file); (2) Data cleaning – removing unwanted or irrelevant data; (3) Data wrangling – preparing the data for analysis by cleaning it further and handling outliers; (4) Data preparation – getting the cleaned data ready for exploratory data analysis (EDA); (5) Performing EDA to understand data characteristics; (6) Model training on the prepared data; (7) Model testing and finally making the model ready for deployment. The video emphasizes the sequential nature of these steps and the importance of each phase in moving from raw data to a deployable ML model.
