How Did I Get Started With Machine Learning?
How Did I Get Started With Machine Learning? Moeedlodhi Follow Sep 12 · 5 min read

My experience on How I got started and gradually learned the basics.

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“The best way to learn Machine Learning is by DOING IT”.If you understand what I mean by this statement, then there is probably no need to go through the rest of the article.

When I was starting, I didn’t know where to begin with. Too much information, too many courses, and just too many different varying opinions on what to do and what NOT to do.

I will be honest, when I googled How to start learning machine learning(or something like that)for the first time back in 2019, I was bombarded with a plethora of varying opinions and different ways to get started.

Some said to learn R, Some said to start with Python, Others recommended getting a Masters degree, while a few downright told me to take a different career path. Yea, I know, varying opinions.

All of this led to an Information overload and left me more confused than ever on where to begin with.

BUT I knew I had to start somewhere and I did.

Made a ton of mistakes and still make them to this day but mistakes are good if you learn from them and make yourself better.

It’s an iterative process which makes you better along the way.

So without further delay, here are is my step by step guide on how to get started with Machine Learning:

Start with Statistics

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Learn Statistics, Clear your concepts around statistics especially pertaining to machine learning algorithms.

When I was starting out, I thought my job would be confined to “programming” only and that there was no need to dive deep into the mathematics as I had Libraries to take care of that. Clean the data, fit a Linear Regression Model, and job well done.

Well, I was WRONG.

A clear understanding of Statistics is a MUST and I would like to give an example of why it is.

When I was practicing with Linear Regression, I read somewhere that Outliers can pose a problem to it but I did not know how to detect and deal with them.

That’s where I found out that z-score and Inter-Quartile Range, two very important statistic concepts, are used exactly for that, to detect Outliers.

And in another example, I learned How P-values and null hypothesis are extremely important to detect insignificant variables present in our dataset.

And this is just scratching the surface.

So in short, get good with statistics and the book I used to learn the basic concepts is : Introduction to Statistical Learning.

Get comfortable with Python

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Learn Python, Simple as that. There are a ton of resources out there that provide extensive content when it comes to Python.

A lot of people recommend R as well but the thing is, You can’t learn everything and there is no need to learn different tools which perform the same function.

What R can do, Python can do just as well if not better. So instead of dedicating significant time and effort into learning two languages, Focus on mastering one and down the road when you do get the time, Learn R too if you feel like it.

A great place to start learning Python is this tutorial.

Start practicing on Datasets

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Once you have extensively worked on the first two tips, then move onto Kaggle and start implementing what you have learned. I cannot emphasize this point enough.

There is no point in learning a concept if you ‘re not going to implement it.

Use your newly learned coding skills to clean the data, create wonderful visualizations, and fit Machine Learning models. Understand the data and the behavior of the model. Like for example You know, Outliers are bad. Well, Why are they bad? What effects do Outliers have on our model? Be an investigator and be curious, very curious.

Recommnded dataset to get started with:

Classification :Titanic Dataset, Iris Dataset

Regression: Boston House pricing , Auto Dataset

Get real life experience

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So you now know the basics of statistics, you can code and you have a few Kaggle projects on your portfolio as well. congratulations, you’ve made it. Good job, Well done. BUT I hate to break it to you that You are just getting started.

I already went over this point extensively in one of my previous Articles where I mentioned how an internship was an eye-opener for me.

Where I for the first time got the chance to provide Real Value in terms of business to Real People.

Real Life job experience will humble you, pressurize you, break you BUT will teach you A LOT if you are the type who is hungry for learning.

Conclusion

I am no different than anyone reading this article, a student of the field of machine learning who is hungry to get better every single day. As of now, I am working as a full time internee which is quite humbling.

I am still learning and my journey has just started.

NOTE: If you like my writing and the content I post, feel free to share it with your friends and family for that helps me a lot. Thank you :)
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['Machine Learning', 'Data Science', 'Technology', 'Careers', 'Artificial Intelligence']