End to End Hyper Parameter Optimization in Machine Learning | RandomizedSearchCV vs GridSearchCV

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Welcome to the End to End Machine Learning Live Classes by Satyajit Pattnaik.

This is the last class of the series, and below is the Agenda that we will learn in this video:

🔴 Agenda of this call:

✅ 0:00 Introduction
✅ 2:18 Hyper Parameter Optimization Basics
✅ 12:27 Manual Search
✅ 18:38 Grid Search vs Randomized Search
✅ 29:39 Manual Search (Practicals)
✅ 51:54 Randomized Search (Practicals)
✅ 1:10:16 Grid Search (Practicals)

1. End to End Machine Learning
2. Classification Algorithms & Practical's
3. Regression Algorithms & Practical's
4. Clustering Algorithms & Practical's
5. Time Series Analysis & Forecasting & Practical's
6. Hyper Parameter Optimization
7. Feature Engineering
8. Projects with Deployment (AWS)

📈 Some Classic end to end ML Projects

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Check Out Our Other Helpful Videos:😍

➮ Data Analyst vs Data Scientist vs Data Engineer - Roles || Responsibilities & Skills
➮Live Implementation of End To End Machine Learning Project With Deployment | Customer Churn
➮ Build your own Alexa in 30 minutes using Python | NLP | Data Science

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More & more great stuffs coming soon, keep supporting & learning 🎓

THANKS FOR WATCHING 😊

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[SupriyaBhowmik-zk]

Thank you for the insightful machine learning video series! Your clear explanations have sparked my curiosity and deepened my understanding of the subject. I'm truly grateful for the knowledge you've shared.

[Djpapaturndownforwhat]

This was very nice, do keep uploading more and more content🙌🏻

[journey-of-learn]

Thank you 🙏🙏🙏
Your video help me lots....
To understand.. The data analyst... Maind set.. And roll.... 🤘🤘

[sonujack]

Thank you sir. Soon I'll be joining your course too. Not for videos but for that Whatsapp channel.

[JankoXA]

Great video. Thanks a lot. Can you share the code for HPO?

[ghostofuchiha]

Hi Satya,
I just want to know how does a ML model handle data once its deployed in production.? Like when we build a model we scale the data, remove nulls, transform it and then use it, but how does all this happen in already deployed models? Because a normal day to day life will have all the uncleaned data. Please help, I m really confused. I can build the ml, dl, transformers etc but am confused how is data preprocessing tackled after model is deployed .

Basically how is all preprocessing captured in the model to be used after deployment, is it through columntransformers and pipelines or are there any other steps or is it under mlops umbrella ?