Machine learning at a broad level is categorized into two types: supervised and unsupervised learning. As the name indicates, this categorization is based on the availability of the historical data or the lack thereof. In simple terms, a supervised machine learning algorithm depends on the trending data, or version of truth. This version of truth is used for generalizing the model to make predictions on the new data points.
Let's understand this concept with the following example:
Figure 3.1 Simple training data: input (independent) and target (dependent) variables
Consider that the value of the y variable is dependent on the value of x. Based on a change in the value of x, there is a proportionate change in the value of y (think about any examples where the increase or decrease in the value of one factor proportionally...
