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Welcome to Episode 20 of Data Science & AI Weekly!

Join Manav and Deepesh find out the right approach towards learning ML, where to begin and how to target developing your ML skills.

TIME-STAMPED SHOW NOTES:

[00:10] Topic of Discussion: How to get started in Machine Learning?
[00:30] Series Recap
[01:06] Welcome Deepesh! Senior Faculty at INSAID
[01:40] How to get started with ML tools?
[01:50] Checkpoints to cover to get started
[02:53] Objectives and approaches of an ideal ML classroom
[04:53] How much math goes into ML?
[05:54] Wrap Up!
[06:00] Learn more about Data Science at www.insaid.co

You can follow the podcast here.

Manav: Are you planning to master Machine Learning? And are you planning to become a Machine Learning Scientist or a Data Scientist?

We have the solution in this podcast because in this podcast we are going to continue where we left in Episode 19 of Data Science & AI Weekly.

Hi everyone, my name is Manav and I’m the Chief Data Science Mentor at International School of AI and Data Science and this is Episode 20 of Data Science and AI Weekly.

If you’ve not subscribed to this podcast, just go to the subscribe button and subscribe immediately so that you don’t miss the next episode when that is going to get released and we release quite a few episodes on a weekly basis.

So in this episode, we’ll continue with our conversation with one of our star faculty at INSAID, Deepesh Wadhwani. So we were having a chat in the previous episode with Deepesh on his journey. Now we are going to take a turn and we are going to talk about getting you prepared for one of the Machine Learning tracks that he does, which is Machine Learning Foundation. So welcome Deepesh again to Episode 20.

Deepesh: Thank you so much for having me here. You talked about the previous discussion we had where we were talking about my journey. What I hope to do is with example, with my own example, I hope to excite you people about Machine Learning, about Data Science, about AI, whatever name we give it.

Manav: Fantastic, right!

So, let’s talk about now, the Machine Learning Foundation track, which is an extremely crucial track for our students because before Machine Learning track, what they have done is they have undergone data analysis, two months of training, and they have gotten started in statistics by turn, they have learned NumPy, pandas, etc.

Now, what are some of the checkpoints that a student needs to keep in mind or you would recommend that these are some of the things that they should definitely know or be good at when they are starting their Machine Learning journey?

Deepesh: Alright, so instead of using the word good at when we are starting the Machine Learning journey, let’s use the phrase are accustomed to.

So let’s see what Data Science broadly encompasses the data and the data will have something that we call explanatory variables. And hence the one thing we should definitely have before coming to Machine Learning foundation is how do we look at the data? So the statistics, the mean, mode, medians of the data, how do we present this data in a Jupiter notebook?

So, Pandas, DataFrame, statistics, Python and the third important thing is logic we should think about where this data is coming from especially in our own domain. So these are the three basic things which I think everybody should bring along with them before we come to the very first introduction to Machine Learning session.

Manav: Alright, so now let’s talk about the Machine Learning foundation track that you take. Tell us a little bit about what is going in your mind and what is your objective when you are teaching the Machine Learning Foundation track. And why do you think that students love sitting through your class so much?

Deepesh: So one of the major challenges is that Machine Learning as a field has a lot of math. So if we open any Machine Learning textbook, a lot of it will be equations.

The first challenge is to translate that language called maths into a language called English. So the one thing which I make sure of is that the equation is translated into what we understand our own domains. So that is challenge number 1. challenge number 2, of course, is whenever we have a classroom full of, let’s say, 50-80-100 students, everybody else will be at a different level.

Everybody else will be from a different domain. How do we manage the opinions of one domain? How do we make someone understand let’s say, who’s from banking, a problem statement, which is from let’s say, mechanical engineering?

So all the problem statements should be simplified for all of us to understand them very well. So these are two basic challenges that we face but that’s the fun of it until we understand what is going on with others we will not be able to be inspired on what to do next in our own work.

Manav: Right. So good that you mentioned both of these points/challenges. So these are definitely you know, the first challenge that a lot of software engineers face when they’re getting started in Data Science.

So let’s talk about particularly challenge number 1 you said that Machine Learning is a lot of maths now there is one group of experts which says that till the time you don’t go deep in maths, you can’t call yourself Data Scientists and they are like math-focused center.

They think that Data Science is nothing but else but maths. And then there is another group of experts who says that for the focus of Data Science overall and Machine Learning, you need to have a very practical real-world approach to Data Science, right.

So instead of getting to centered around Math, what do you think and what is the approach that you take in your classes when you’re teaching?

Deepesh: So let me translate the question you asked in a little different words…

Is art about Hindi or English? It’s not, right?

Art is art. If you want to write a poem, it will be a poem. The expression is what is important. Math and the business, the business sense, these both are translation of the same problem in two different languages.

So Data Science, for me, is the logic.

It’s the way we decide things and not how we write it. So an expression can be written in a mathematical equation. And that exact expression can be written in the English language too. So what I hope to do is build the logic and hopefully in the next 3 months, as we are studying Machine Learning together, we will be able to translate English to Math and Math to English very comfortably.

Manav: So this is Episode 20 of Data Science and AI Weekly!

I am in chat with Deepesh and we will be back with another episode, Episode 21st of Data Science and AI Weekly in which I’m going to continue my conversation with Deepesh and ask him a few more questions that I would personally want to ask him on your behalf.

Thank you very much for tuning in and see you in Episode 21. Thanks for tuning in. This is Manav. I’m signing off.

Author

Content Writer @ INSAID. A machine learning buff who loves to read, write and explain everything AI!

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