🔥Machine Learning Training with Python:
This Edureka video on Ensemble Learning covers the basics of Ensemble Learning Methods. You will learn about the underlying working of Boosting, Bagging, and Voting algorithms. The video will make you learn how to create a model using Ensemble Learning validate it using cross-validation.
These are the following topics that are covered in this Ensemble Learning Tutorial video:-
00:00:47 What is Ensemble learning?
00:01:49 Need For Ensemble Learning
00:02:31 Bias – Variance Tradeoff
00:04:03 Ensemble Learning Techniques
00:09:00 Ensemble Learning In Action
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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 24×7 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
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.
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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