➡️ The first step is 🎯 defining the problem 🎯 we are trying to solve and determining the specific objective(s) we are trying to achieve. This helps maintain focus and establish the right scope of the project for everyone involved. Naturally, this also comes with the necessary explanation of what can and cannot be achieved using Machine Learning.
➡️ The next step is 🎯 collecting and preparing the data 🎯 that will be used to train the ML model. This typically includes cleaning the data to remove errors or inconsistencies, and selecting the most relevant and informative features to use in the model. This is often also the most intensive step in an ML project.
➡️ Once the data has been prepared, we need to 🎯 choose a suitable algorithm 🎯 to train the model. There are many different algorithms to choose from, and the right choice depends on the specific problem we are trying to solve and the type of data we are working with.
➡️ Once an algorithm has been chosen, we 🎯 train the model 🎯 using the prepared data. This involves feeding the data into the algorithm and adjusting the model parameters to find the best "fit". After the model has been trained, its performance can be evaluated using various metrics to see how well the model can make predictions.
➡️ Even after the model has been trained and evaluated, there is still room for improvement. We can 🎯 refine the model further 🎯 by adjusting parameters and testing different combinations to see which produce the best results. The criteria for determining whether a model is acceptable depend on the specific business objectives and requirements of the project.
➡️ Once the model performs satisfactorily, it can be deployed in a 🎯 production environment 🎯 and used to make predictions and solve real-world problems.