Regular attendees of tech conferences might have noticed that pushing toward AI and machine learning is one thing that's consistent across all tech giants, including Apple , Google , Amazon, and Microsoft.

For example, this year's Google I / O was largely focused on machine learning, artificial intelligence, and how it's going to revolutionize the world.

And me, as an Android developer and someone who has a bad case of FOMO, I started looking into any and all resources that could help me get a kickstart with using machine learning and AI in my development projects.

Now here’s where things got interesting—a majority of courses and tutorials available online focused heavily on the math behind machine learning, which, to be honest, is as dull and as boring as machine learning sounds interesting and exciting.

Come to think of it, he does look really high

Now don’t get me wrong here, I’m definitely not advocating that you don’t need to learn all the math behind machine learning. What I want to outline here instead is that jumping into learning all these things directly requires a lot of commitment and time dedication from your side.

As a full time app developer, you always need to stay up to date with the latest happenings in your respective domain. Given this, full time dedication to learning machine learning might not be possible.

Plus, while you're learning all the math and statistics, there's no way to relate things to your real life like we can while learning new concept in Android / iOS. This lack of concrete application can in turn result in lack of motivation to continue.

I personally faced these roadblocks while struggling to get into machine learning and decided to try alternative approach to learning ML.

Thanks to this alternative approach, I was able to hype my brain up and motivate myself enough to go through all the math behind machine learning.

I'll be outlining my entire approach below, so sit tight and grab some popcorn while you're at it;)