In this guest feature, Vatsal Ghiya, CEO and Co-founder of Shaip has discussed some key insights on the importance of quality datasets for creating an effective machine-learning model.
The Key Takeaway from the Article is
- Are you aware of the technicalities involved in creating machine learning(ML) algorithms intuitive, holistic, and impactful? However everyone has always talked about “Finesse” and “Fun” parts of creating a machine learning model, but less is discussed about the functionality. This process involves the pre-processing techniques, basis of data collection, data annotation, and a lot more.
- In layman’s language, ML data is a single entity by the algorithms despite housing disparate chunks of data. And these datasets are fed into the system to train algorithms to identify patterns. Every organization can use these datasets as per their business requirements.
- And to make the machine learning algorithm identify the right and accurate pattern requires quality data sets that must be collected in a format to prepare relevant datasets that include data collection, pre-processing, and annotating. Moreover, these data sets can be collected from multiple sources like govt sources, machine learning depositary, and google datasets engine.
Read the full article here: