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

(I wish I had this before)

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

Also keep these points in mind.

> Machine learning is a vast field, the point of this thread is to help you get started, you'll have to keep that momentum going on afterwards too!

> Consistency is everything, it will decide if you use this curriculum well or not!

> Just because we will not being using any math during this time doesn't mean its not important, it is VERY important but it can come much later on in our journey.

> All courses and other material in this thread are 100% free.

With all that cleared, let's take a look at the curriculum you'll be following for the next one month.

Our goal will be to have a solid understanding of neural nets work without the complex math and we'll also learn how write quite a bit of code in this process.

We will also try to complete a simple kaggle challenge if possible.

You can read more about Kaggle here: https://twitter.com/PrasoonPratham/status/1325124948025724928

Step 1

Your first 10 days will be spent learning Python from this tutorial

www.​youtube.​com/watch?v=rfscVS0vtbw

Believe me, this is enough to get started with Machine learning, you can learn more complex python concepts when you need them.

30-45 minutes a day and you'll finish this course in about a week and a half or so, take longer if you need to, everyone has a different learning rate.

Here's a pro tip, incase the setup for python is too hard, you can use repl.​it, an online editor where you can write Python code without any setup. make sure you are using Python3 (3.7/3.8 is recommended) and not Python 2.

Step 2

We'll now dive in into how neural networks work!

This series by 3blue1brown has a simple, intuitive and a visual approach to how neural networks works without ANY complex math at all

www.​youtube.​com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi

It won't take you longer than 3-4 days to complete this series.

I must also emphasise that along this journey you'll have to enjoy everything otherwise you will not be able to learn things as quickly as you could.

Step 3 : We're now about 17 days into our machine learning journey, here's the course you have to take next.

Machine learning foundations by google developers, I can't recommend this course enough, it helped get started with machine learning in a way no other course could.

www.​youtube.​com/watch?v=_Z9TRANg4c0&list=PLOU2XLYxmsII9mzQ-Xxug4l2o04JBrkLV

These set of 12 videos in 12 days will take your machine learning skills from zero to 1, it really is a great course to take.

Did I mention you'll also learn how to use Google Colab and Jupyter notebooks in these courses

We have one day remaining!

Our goal today is try and complete a practical project on kaggle, here's the competition you want to compete in as a beginner : Digit Recognizer challenge.

You've already trained a MNIST classifier in the google developers course, you'll have to apply that in the practical world using this challenge.

www.​kaggle.​com/c/digit-recognizer

Tip : Take help from submissions from other people incase you are stuck. Also do not forget to google things when needed!

Our one month of machine learning has ended and we've learnt quite a bit and that is awesome!

I wish you all the best in all your future endeavours.