Although machine learning is mainly linked to the data-modeling step of the data science process, it can be used at almost every step. the data science process is shown below
The data modeling phase can’t start until you have qualitative raw data you can understand. But prior to that, the data preparation phase can benefit from the use of machine learning. An example would be cleansing a list of text strings; machine learning can group similar strings together so it becomes easier to correct spelling errors.
Machine learning is also useful when exploring data. Algorithms can root out underlying patterns in the data where they’d be difficult to find with only charts.
Given that machine learning is useful throughout the data science process, it shouldn’t come as a surprise that a considerable number of Python libraries were developed to make your life a bit easier.
Machine learning is also useful when exploring data. Algorithms can root out underlying patterns in the data where they’d be difficult to find with only charts.
Given that machine learning is useful throughout the data science process, it shouldn’t come as a surprise that a considerable number of Python libraries were developed to make your life a bit easier.