During the summer of 2021, I spent some of my time studying Machine Learning and Data Analysis. Within that period, I played with many ML models such as Linear Regression, Decision Tree Regression, Random Forrest Regression, Logistic Regression, and many more. I have made some predictions, visualizations, and classifications on numerous data sets on the web. Despite having experience in the discipline, I had little to no intuition about its mathematical foundations. I was only familiar with Linear Regression in Statistics but other than that I was clueless.

 It was only when I learned about Bayes Theorem that I had my first glimpse into the inner workings of Data Science. I was amazed by the role of Probability Theory especially Conditional Probability in computing the Bayes Theorem formula below.

 The spam filter and the disease problems also worked great in facilitating my understanding as I have also known that these are some applications of Machine Learning. This lesson has further cemented my belief that Artificial Intelligence is heavily interrelated with Statistics.

 Learning Bayes Theorem in this chapter really opened my eyes to how mathematical concepts play a fundamental role in these technologies. It also made me appreciate the level of abstraction I’m working with when I code on a Jupyter Notebook. My perspective has completely changed and I am grateful that