-
Machine Learning
Mathematics
Statistics
Graphics
Data Science
Machine Learning
TensorFlow
Example 1
Example 2
JS Graphics
History
Machine Learning Mathematics
The main branches of Mathematics involved in Machine Learning are
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:
You should focus on the mathematic concepts described here.
Linear Functions
// Generate values var xValues = []; var yValues = []; for (var x = 0; x <= 10; x += 1) { xValues.push(x); yValues.push(x); } // Display using Plotly var data = [{x:xValues, y:yValues, type:"lines"}]; var layout = {title: "f(x) = x"}; Plotly.newPlot("myPlot1", data, layout);
Graphics
- Graphics plays an important role in Math
- Graphics plays an important role in Statistics
- Graphics plays an important role in Machine Learning
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 | ||||||||||||||||||||||||||
|
|
Probability
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
Statistics
Statistics is about how to collect, analyze, interpret, and present data.
Statistics works with questions like:
