Project introduction

0:00 this into toin machine learning course will help you with Core Concepts and advanced mlops integration IU sing will guide you through creating a project to help you learn data analysis feature engineering and model implementation with rigorous testing while also teaching you how to write scalable and readable code finally you'll learn how to integrate ml Ops using tools like Zen ML and ml flow for seamless experiment tracking and employment what if I tell you that you can build a house price prediction model and still be in the top 1% of the data science community and back a classy data science job I mean that sounds too good to be true right but it's actually true most of the people say that go with a better idea everybody builds house price prediction model but in this video I'll show you that how you can take a simplest Pro idea but with the implementation you can make it better than even the one which has the best idea out there will build an entire end to end machine learning pipeline all the way starting from core ml to integrating mlops principles like cicd experiment tracking monitoring and

1:10 much more but Frankly Speaking the 99% of the people what do they do they simply go ahead take the data it import it and then probably do some Eda just by sake of doing it and once they have some sort of nice sexy graphics and all those things they simply go ahead and then do some processing which is written by so-called gurus on YouTube and then they simply create the project by calling fit and then they call predict and that's where the end of the project means so even if you take the sexiest idea of your time you cannot really have that in your portfolio or be in the top 1% despite having the nice idea it's all about the implementation so what we will do differently into this process there are two aspects of it one is core ML and another one is mlops principle in core ml will entirely focus on first of all understanding data because when I was

1:57 working in one of my company one of my manager told me that there there's no point in doing data science if you do not spend your most of your time in working on the data so we'll spend the most amount of time in working on the data understanding the data I'll guide you through my thinking and decision process of why we are making such decisions or analysis and based on those analysis we will interpret those things in order to know what can be issues what can be things to be done what sort of algorithm to choose and if we choose those algorithm how do we verify whether that data fits for the algorithm which is known as assumptions testing and violation and much more into the core ml part into the mlops principle we'll integrate cicd pipelines deployment using ml flow for experiment tracking using zml for orchestration and all those things and this one important

2:42 factor which we'll also focus on is writing better code because nowadays all the people right now in the community they do not follow the right coding standards or the principles or the architectures out there we'll make use of design patterns to write code and to actually use the right set of patterns in order to implement such scenarios and all of this which is core ml mlops principles and using the right design patterns into the project and writing better code would make this the project the top 1% and to be honest our several students just got their first job by just doing this so this is the project which is a testament of implementing it right no matter what the idea is so let's get started with the project hi everyone so we are finally going to start our uh series on building end to