Now that we have covered the basics and different types of machine learning algorithms, let's discuss the workflow in data science. A typical workflow looks like the following:
- Problem definition: Typically, any data science and machine learning project starts with problem definition. In this first step, you need to define the problems that you are trying to solve with data science, the scope of the project, and the approaches to solving this problem. When you are thinking about some of the approaches to solving your problem, you will need to brainstorm on what types of analyses (descriptive versus explanatory versus predictive) and types of learning algorithms (supervised versus unsupervised versus reinforcement learning) ...
