How did I get started with learning machine learning?

Believe you can and you’re halfway there.

— Theodore Roosevelt

“The best way to learn Machine Learning is by DOING IT.”

To be honest , the first time I heard the word “Machine learning”, I had no idea what it was. I was not mature enough to understand things by googling about it, I realised they were the buzzwords predominantly in most of the domains.

What made me learn Machine learning ?

Being curious and enthusiastic about learning tech, you need to agree to the fact that staying updated is the key. I always wondered how technologies work in personalised products, movies and video recommendations?

How assistans like Cornata, Alexa and Google Assistant recognise my speech? How are emails classified into spam and non-spam mails?. How does Facebook or Instagram suggest automatic friend tagging? How Google forecasts traffic, weather, and other variables…

Where did I learn ML ?
When I started to look for resources , I was bombarded with a surplus. There were too many varying opinions. In fact it took more time for me to choose or stick to a particular structured resource .

We call it Machine learning because we are not telling the machine what to do, instead we are teaching the machine how to do it.

Arthur Samuel coined the term “Machine Learning” in 1959 and defined it as a “Field of study that gives computers the capability to learn without being explicitly programmed”.

Accepting facts : Maybe you need to agree to this phrase: “Machine learning is Maths.”

Step-by-step achievement:

● Deeper understanding of machine learning concepts and algorithms needs some fundamental knowledge of mathematics. So I started learning Linear algebra, Calculus and Probability along with statistics

● Gradually geared up for my journey, I chose Python as the programming language for implementation because Python was easy to code, beginner friendly and compact-able

● Finally into tech, where I started to understand the types of Machine learning, algorithms and most importantly, understanding the technical terms like features, label, model, dataset, training, testing, prediction…

Conclusion
Gradually becoming a pro from newbie by understanding various algorithms and to invoke industry experience, I implemented some possible small-real world projects.
I recall how excited I was when I first started using machine learning to predict house prices and the joy of predicting future value.
I wish I could link to the resource i used, but I wanted you to follow your own path, so

Explore a lot….Learn a lot….and Implement a lot