Something I've encountered frequently in the math for machine learning and number theory.
Here's how it works.
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This weirdly shaped 'E' is the capital greek alphabet called 'Sigma' and it stands for summation.
Essentially what it allows us to do is represent equations where we add multiple items, let me show you what I mean.
Let's say we have to add squares of numbers from 1 to 10, you'd write it as an equation like this.
(The square of a number is basically multiplying the number by itself, like 2ยฒ, read as 2 squared = 2 x 2 =4)
You can write the same equation using Sigma notation.
Doesn't it look cleaner and shorter?
I like to think of Sigma as a 3 step process (like a 'for' loop if you're a programmer), you start from the bottom and then go towards the top.
In this case:
1. You start with i=1 (on the bottom) 2. Square it 3. Add 1 to the previous value of i 4. Square it 5. Add this to the previous squared number 6. Repeat until i reaches the number on the top which is 10 here.
At times you will notice a number in front of sigma, this basically multiplies with the entire equation with that particular number.
I first encountered sigma in the RSME (root mean squared error) formula, a commonly used metric while training machine learning models.
Before we bring this thread to end, try solving this sigma equation.
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You'll see it in a lot of machine learning formulas, computer science, and math in general.
Here's a really simple explanation of how it works.
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Before we understand how logarithms work, it is important to know how 'exponents' work.
aแถ=b (read as "a to the power of c equals b") is an exponent where a,b and c are numbers.
aแถ=b just means that a multiplied by itself c amount of times is equal to b.
Here are some examples:
- 2ยฒ basically means 2 multiplied by itself 2 times which is 4
- 2ยณ basically means 2 multiplied by itself 3 times which is 8
Everyone tells you how important math is in machine learning.
But I believe that if you want to learn machine learning today, there are things more important than math which very few people will tell you about.
Here are 5 of them.
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The field of machine learning has been very academic which is why there is so much emphasis on learning math for it.
But, today with the frameworks that we have, not knowing how to multiply matrices is far less of an issue than knowing how to use the tools in this list ๐๐ป
Yes, I know; you have R, Julia, and whatnot for machine learning but as of today, Python is the industry standard.
Get your data structures and algorithms right in Python because you will need them, here are some concepts worth learning ๐๐ป
This is a step-by-step guide on how you can solve the Titanic disaster challenge on Kaggle.
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Kaggle challenges are a great way to practice your machine learning skills.
In this thread, we'll go through each step for solving the beginner friendly titanic disaster challenge.
These are the key steps that we will go over:
- Cleaning the data
- Training a machine learning model using decision trees in Sklearn
- Making a submission to Kaggle using the predictions from our training model