Question

what is the sequence of the steps in the machine learning process

Answer 1

There are seven steps to machine learning:-

1.) Data Collection

Machine learning requires training data, a lot of it. Data collection, or datafication, is also the first step in my new D2 + A2 model.

2.) Data Preparation

Raw data alone is not very useful. The data needs to be prepared, normalized, de-duplicated and errors and bias need to be removed. Visualisation of the data can be used to look for patterns and outliers to see if the right data has been collected or if data is missing.

3.) Choosing a Model

The third step consists of selecting the right model. There are many models that can be used for many different purposes. Upon selecting the model, you need to make sure that the model meets the business goal.

4.) Training

Training your model is the bulk of machine learning. The objective is to use your training data and incrementally improve the predictions of the model.

5.) Evaluation

This step entails testing the machine learning against an unused control dataset to see how it performs. This might be representative of how the model works in the real world, but this does not have to be the case.

6.) Parameter Tuning

After evaluating your model, we should test the orginally set parameters to improve the AI. Increasing the number of training cycles can lead to more accurate results. However, you should define when a model is good enough as otherwise, you will continue to tweak the model. Hence, it's an experimental process.

7.) Prediction

Once we have gone through the process of collecting data, preparing the data, selecting the model, training and evaluating the model and tuning the parameters, there can be all kinds of predictions, ranging from image recognition to semantics to predective analytics.