ML Mathematics
The main branches of Mathematics involved in Machine Learning are:
 Linear Functions
 Linear Graphics
 Linear Algebra
 Probability
 Statistics
Machine Learning = Mathematics
Behind every ML success there is Mathematics.
All ML models are constructed using solutions and ideas from math.
The purpose of ML is to create models for understanding thinking.
If you want an ML career:
 Data Scientist
 Machine Learning Engineer
 Robot Scientist
 Data Analyst
 Natural Language Expert
 Deep Learning Scientist
You should focus on the mathematic concepts described here.
Linear Functions
 Linear means straight
 A linear function is a straight line
 A linear graph represents a linear function
Graphics
 Graphics plays an important role in Math
 Graphics plays an important role in Statistics
 Graphics plays an important role in Machine Learning
Learn more about linear functions ...
Linear Algebra
Linear algebra is the bedrock of data science.
Knowing linear algebra boosts your ability to understand data science algorithms.
Scalar  Vector(s)  
1 


Matrix  Tensor  


Learn more about linear algebra ...
Probability
Probability is how likely something is to occur, or how likely something is true.
I have 6 balls in a bag: 3 reds, 2 are green, and 1 is blue.
Blindfolded. What is the probability that I pick a green one?
Number of ways it can happen are 2 (there are 2 greens).
Number of outcomes are 6 (there are 6 balls).
The probability is 2 out of 6: 2/6 = 0.333333...
Probability = Ways / Outcomes
Learn more about probability ...
Statistics
Statistics is about how to collect, analyze, interpret, and present data.
Statistics works with questions like:
 What is the most Common?
 What is the most Expected?
 What is the most Normal?
Learn more about statistics ...