Decision Trees and Hyperparameters | Solving a real-world problem from Kaggle

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💻 In this lesson, we learn how to use decision trees and hyperparameters to solve a real-world problem from Kaggle. You can experiment with the notebook used in the above video here 👉 https://jovian.ai/aakashns/sklearn-de...

🔗 Check out this playlist for the complete lecture series on Gradient Boosting Machines:    • Machine Learning with Python: Zero to GBMs  

🎯 Topics Covered
• Downloading a real-world dataset
• Preparing a dataset for training
• Training and interpreting decision trees

⌚ Time Stamps
00:00 Introduction
08:41 Downloading a real-world dataset
13:36 Preparing a dataset for training
34:51 Training and interpreting decision trees
01:05:25 Overfitting, hyperparameter tuning & regularization
01:30:24 Intro and Outro
01:47:06 What to do next?

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⚡ Free Certification Course
"Machine Learning with Python: Zero to GBMs(Gradient Boosting Machine)" is a practical and beginner-friendly introduction to supervised machine learning, decision trees, and gradient boosting using Python. You will solve 3 coding assignments & build a course project where you'll train ML models using a large real-world dataset. Enroll now: https://zerotogbms.com

🔗 Visit the decision trees and hyperparameters lecture page here: https://jovian.ai/learn/machine-learn...

🎤 About the speaker
Aakash N S is the co-founder and CEO of Jovian - a community learning platform for data science & ML. Previously, Aakash has worked as a software engineer (APIs & Data Platforms) at Twitter in Ireland & San Francisco and graduated from the Indian Institute of Technology, Bombay. He’s also an avid blogger, open-source contributor, and online educator.

#GBM #MachineLearning #Python #Certification #Course #Jovian

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