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.
๐งต ๐๐ป
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
So on and so forth.
Logarithms are basically a way of representing exponents in a different format.
Let me show you what I mean, both the formulas that you see below are equal.
logโb = c is basically saying that "a to the power of c = b".
Dare I say, logarithms are exponents in disguise.
Here's a small exercise for you, what is the value of c in the below equations?
I've put them in logarithmic and exponential notation to make it easy for you.
Logarithms are used in all sorts of places, machine learning, computer science (time complexities)... you name it.
They are also a bunch of logarithmic properties that we can use to our advantage, but that is something I'll discuss in another thread.
That is the end of this thread.
Consider following @PrasoonPratham if you like bite-sized content like this on machine learning, math, and computer science.
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.
๐งต ๐๐ป
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.
๐งต ๐๐ป
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