These course notes were developed using lectures/material from the ML Specialization from Andrew Ng. The visuals and equations are adopted from the original slides. I simply hope that the notes serve as accompanying study material. Find all my notes for this course in the ML Course Notes repo.
Machine Learning (ML) is a subfield of Artificial Intelligence (AI). It deals with training learning algorithms to deal with all sorts of predictive problems and tasks.
According to a study by McKinsey, AI and machine learning is estimated to create an additional $US 13 trillion of value annually by the year 2030.
ML is already creating major changes in the software sector. However, there is a lot of progress happening in automotive, transportation, retail, and other sectors as well.
Because of the massive untapped opportunities across many sectors, there is high unfulfilled demand for machine learning engineers and related skillsets. This course aims to teach the fundamentals of machine learning. It also emphasizes on how to put the knowledge into practice.