1. Formatting: The data is spread in different formats. Formatting will bring it together in one sheet. For example, customer data can come with different currencies, languages, etc. These need to be compiled under one format.
2. Labeling: ...

Testing data is used to test the validity of the training data set. Training data is not used for testing because it will produce the expected output. The testing data set comprises of 20 percent of the total data.

The process of curating datasets for machine learning starts well before availing datasets. Here’s what we suggest:

• Identify the goal of AI
• Identify what dataset you will need to solve the problem
• Make a record of your assumptions while selecting the data
• Aim fo...

If you have a small dataset, using a model pre-trained on large datasets can be a good idea. You can use your small dataset to fine-tune it.

Validation tests are used to identify and tune the ML model.

Data is the new oil - and just as oil needs the right refining to come into perfect usage, data too needs curing. The power of your machine learning models will greatly depend on the quality of your data.

ML engineers depend on data during each step of their AI journey – from model selection, training, and tuning to testing. These datasets usually fall under three categories:

1. Training sets
2. Testing sets
3. Validation sets