Model Selection & Boosting | Machine Learning Tutorial | Edureka Rewind

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This Edureka video on Model Selection and Boosting Step by step guide to select and boost your models in Machine Learning.
Topics Covered :
00:00:00 Introduction
00:03:45 Agenda
00:04:00 Model Selection
00:06:35 Need For Model Evaluation
00:15:38 Resampling techniques
00:19:40 Hyperparameter Tuning
00:26:02 Manual Search
00:27:13 Grid Search
00:28:45 Random Search
00:31:20 Metrics for Evaluating Regression Models
00:37:46 Metrics for Evaluating Classification Models
00:49:05 How to Calculate a Confusion Matrix
00:53:33 Interpreting a confusion Matrix
00:55:35 Issues with Confusion Matrix
00:58:00 What is ROC?

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About Course:
Why Learn Python for Data Science?
Python has been one of the premier, flexible, and powerful open-source languages that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, It has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. It's continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger.