Explore and experiment in order to learn about AI/ML/DL
Learning Paths to complete mastery of AI/ML/DL and ultimately building a pure AI/ML/DL application This is a tall goal but in the plan to become familiar with the end to end build, I have some basic ideas of how to get there and what the necessary steps I should take. This repository serves as a documentation of the journey to get there.
Roughly, I plan to tackle the following key components to setting up the ML pipeline through the following:
- Data Extraction ( Some sort of web-scraping or set up a database of collecting data )
- Data ETL ( Cleaning and merging the data in need and prepare them for the later analytics )
- Data Exploratory Analysis (In this component, data needs to be further transformed and whipped into the right shape, Data visualization is also an important sub-category. Some sort of dashboarding could be built as a result to better represent the underlying data)
- Feature Engineering (Based on the understanding of the data from EDA step, key variables need to be extracted and prepared for later model building)
- Model Building (Running various machine learning techniques and build models to achieve certain accuracy. Models could also include Deep Learning algorithms etc.)
- App Building (App building in itself is a separate category but the purpose of putting this here is to help evolve the model built into an end-user friendly, consumable product. Models, data refresh, model refresh all need to be baked into production )