Model Selection & Boosting | Machine Learning Tutorial | Edureka | ML Rewind - 2

🔥Machine Learning Training with Python: https://www.edureka.co/machine....-learning-certificat
This Edureka video on Model Selection and Boosting Step by step guide to select and boost your models in Machine Learning.
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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How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!

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About the Course :

Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence.
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Why Learn Machine Learning with Python?

Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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