Machine learning is the science (and art) of programming computers so they can learn from data” by Aurélien Géron
Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.
Y = f(X)
The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data. Link:https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/
Is when you only have input data (X) without a corresponding target variable (y) to predict The aim is to model the underlying structure of the data in order to learn from data and identify groups of data (segments / clusters) with similar characteristics / behaviours
What is Supervised Learning (Supervised Vs Unsupervised Learning) Problem formulation - What are we trying to solve? Loading the Raw Data into Python Cleaning the Raw Data Splitting the data set for training depending on the type of machine learning approach Train the Model Evaluate and Optimise the model Report the results
#Core Tools and Tech Used Explaining how the whole automated process will work (Excel -> SQL -> Python -> SQL -> Power BI)