Let's recap all the steps involved in training a machine learning model:
Reflect on your use case and consider whether ML can be (part of) the solution to the problem you are trying to solve.
Source the data you need and take every possible step to minimise the potential impact of biases in the data.
Clean and prepare the data so that it contains the right balance of information the model needs to learn from.
Choose the algorithm that is best suited to meet the goals of your use case and the features of your training data.
Upload the dataset you prepared to the algorithm of your choice and wait for it to learn.