100 Days of Machine Learning

July 22 2019

Vector graphic of a computer with TensorFlow's logo on the screen


Spring 2019, I took my first data science class as a computer science minor. The course essentially was an introduction to Machine Learning and ML algorithms. I had a lot of fun learning R and working on the pre-installed data sets. Our class learned about the different methods of learning: supervised, unsupervised and reinforcement. The class was mainly focused on supervised learning and classification problems. By the end of the semester, I had a solid understanding of machine learning foundations. But I felt that I lacked the proper mathematical knowledge to apply these principles to my own projects.

This summer I decided to take a deeper dive into machine learning by starting right from the basics. I will be exploring various mathematical topics including:

Additionally, I would take time to create interactive visualizations and notes to drive the concepts home.

Day 1 - 8

Linear Algebra

Found Khan Academy's amazing linear algebra course today. Learned that Vectors are just objects that have a direction and magnitude. They do not necessarily originate from the origin. I Also learned basic vector operations.

Adding Vectors

Vector 1 + Vector 2 = Vector 3

Adding vectors results in a vector that shows the displacement of the combined vectors

Multiplying and Dividing Vectors

Vector 1 × Scaling Factor = Vector 2

Multiplying vectors results in a scaled vector in the same or opposite direction

Subtracting Vectors

Vector 1 + (-1 × Vector 2) = Vector 3

Subtracting two vectors results in a vector which equals the distance between them

Parametric Equation of a Line

Vector 1 + (-1 × Vector 2) = Vector 3

A line in any dimension can be defined by two vectors using the parametric equation of a line

Day 9

Day 10

Day 11

Day 12

Day 13

Day 14

Day 15

Day 16

Day 17

Day 18

Day 19

Day 20

Day 21