I recently got the opportunity of working at a start-up with a very good pay, even for a college graduate.

I declined the offer.

Here's why.
Here's a bit of the backstory, I was approached was the co-founder of this start-up through a twitter DM. The start-up is more than 1 year old at this point and was generating revenues of $250K + a year and was profitable! (at least what I was told in the call)
The start up is also based in India and I was offered a remote dev job as a machine learning engineer. This seems like a very good offer but ended up declining it.

1. I'm too young for it

This might sound cliche but I'm just not ready for working 21 hours/week at a job just yet. I feel there's lots for me explore and learn.
2. I don't have the time for it

Being a second year student, it is hard for me to manage time as is, how would I manage my studies along with a job πŸ€·β€
3. I the ideologies of the start-up did not resonate with me

I disagreed with the fundamental ideologies the start-up was based upon like their business plan itself.
I saw many, many red flags.

I'll leave it at that.
Second year high school student*


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More from @PrasoonPratham

22 Nov
Here's what your first 30 days of Machine Learning should look like.

(I wish I had this before)
πŸ§΅πŸ‘‡ Image
What you are going to look at now is a curriculum I wish I had followed.

It took me 2 years to get into machine learning because I was never consistent, however you don't have to make the same mistake.

You'll learn a lot in a very short amount of time through this thread.
Before we begin with the curriculum, make sure you throw these myths out of your mind.

> Machine learning does not require creativity

> Machine learning is difficult

> You need to know a lot of math to get into machine learning

> You need an expensive PC for machine learning
Read 19 tweets
20 Nov
There are a lot of myths surrounding machine learning.
When I say a lot, I mean it.

Here are 3 of the most common ones.

πŸ§΅πŸ‘‡ Image
> π˜”π˜’π˜€π˜©π˜ͺ𝘯𝘦 𝘭𝘦𝘒𝘳𝘯π˜ͺ𝘯𝘨 π˜₯𝘰𝘦𝘴 𝘯𝘰𝘡 𝘳𝘦𝘲𝘢π˜ͺ𝘳𝘦 𝘀𝘳𝘦𝘒𝘡π˜ͺ𝘷π˜ͺ𝘡𝘺

This is just straight out wrong.
You have to be creative in finding solutions to obscure problems which you may encounter in machine learning ( and you can build this creativity! )
> π˜”π˜’π˜€π˜©π˜ͺ𝘯𝘦 𝘭𝘦𝘒𝘳𝘯π˜ͺ𝘯𝘨 π˜ͺ𝘴 π˜₯π˜ͺ𝘧𝘧π˜ͺ𝘀𝘢𝘭𝘡

No it isn't. You have thousands of resources online to help you out, not to mention you don't even need crazy hardware to train your machine learning models, all you need is a computer, an internet connection and passion.
Read 5 tweets
18 Nov
Some of the best free machine learning resources I found out over the last 2 years.

Machine Learning Glossary by Google developers
> Has pretty much every definition and explanation for commonly used machine learning terms.

Towards DataScience
> A brilliant resource for reading articles about machine learning and data science

Read 5 tweets
18 Nov
Machine learning Basics πŸ€–

Pandas is one of the most important frameworks you'll be working with if you plan on getting into machine learning.

What is it and where is it used? πŸ€”

πŸ—ƒοΈ In machine learning, you'll often come across data stored in data stored in excel sheets ( or similar kinds of data ).

In order to interact with that data you'll have to use "pandas". 🐼
πŸ“‚ Pandas allows you to read the data stored in these files without changing the original file itself.
Read 4 tweets
17 Nov
Programming is an essential part of Machine learning.

This is why I have decided to start Python quizzes inspired from @ravinwashere's quizzes to boost your problem solving skills.

Here's the first one, write python code that gives the output based on the input.
The first 5 participants who participate (doesn't matter if its wrong or right) will get a shout out πŸ˜‰
This was my attempt
(I know its horrible)πŸ˜…
Read 5 tweets
17 Nov
I made a lot of mistakes during my machine learning journey.

Being a web developer, here's why it took me 2 years to get started machine learning when I could've done it in less than a month.

(and you can too!)
My first attempt of getting into machine learning was in early 2018. I saw a few videos on YouTube but never really understood anything about it.
Terms like "Support Vector machines", "generative adversarial networks" always scared me.

Moreover there was nobody to guide me in the right direction.
Read 21 tweets

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