Exploring Supervised Machine Learning Algorithms

The main goal of this reading is to understand enough statistical methodology to be able to leverage the machine learning algorithms in Python's scikit-learn library and then apply this knowledge to solve a classic machine learning problem. The first stop of our journey will take us through a brief history of machine learning. Then we will dive into different algorithms. On our final stop, we will use what we learned to solve the Titanic Survival Rate Prediction Problem. With that noted, let's dive in! As soon as you venture into this field, you realize that machine learning is less romantic than you may think. Initially, I was full of hopes that after I learned more I would be able to construct my own Jarvis AI, which would spend all day coding software and making money for me, so I could spend whole days outdoors reading books, driving a motorcycle, and enjoying a reckless lifestyle while my personal Jarvis makes my pockets deeper. However, I soon realized that the foundation of machine learning algorithms is statistics, which I personally find dull and uninteresting. Fortunately, it did turn out that "dull" statistics have some very fascinating applications. You will soon discover that to get to those fascinating applications, you need to understand statistics very well. One of the goals of machine learning algorithms is to find statistical dependencies in supplied data.