Ensemble Machine Learning in Python : Adaboost, XGBoost
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 1.12 GB
Genre: eLearning Video | Duration: 36 lectures (4 hour, 2 mins) | Language: English
Ensemble Machine Learning technique like Voting, Bagging, Boosting, Stacking, Adaboost, XGBoost in Python Sci-kit Learn

What you'll learn

Machine learning concept and bias variance error.
Concept behind Ensemble learning and Different types of ensemble learning
Apply voting classifier and voting regressor with Scikit-learn API
Understand and implement bagging ensemble learning method
Apply special bagging ensemble technique Random forest on credit card Dataset.
Learn adaboost and XGBoost ensemble technique
Understand and implement Model stacking technique

Requirements

Basics of Python programming
Knowledge about Machine learning algorithms

Description

Let's say you want to take one of the very important decision in your life, it will be a choosing your career or choosing your life partner.

Do you think that you can depend on a just one person advice. Advice from the one person can be highly biased also. The best way you can go ahead by asking and taking guidance from multiple people which reduce the bias.

Same thing apply on machine learning world also while predicting some class or predicting any continuous value for regression problem, why you should rely on a one model only. support vector machine, neural network, decision tree, random forest logistic regression, genetic algorithm.

This type of many algorithms are available. Why don't we use the capability of many algorithm for prediction. So using those power of multiple algorithm for the prediction is called as ENSEMBLE LEARNING.

So welcome to my course on and Ensemble Machine learning with Python.

One of the most useful technique in machine learning to balance bias and variance.

Reducing Variance & reducing high bias error are such important task while designing the machine learning system and Ensemble learning is the solution behind that.

Why ensemble learning :

Build model with low variance and low bias.

Majority of machine learning competition held on kaggle website won by this and ensemble learning approach.

Nothing new here to invent but depend on multiple existing algorithm to improve model.

What course is going to cover :

Different ensemble learning technique

Simple voting classifier, hard and soft

Averaging ensemble learning technique : bagging and pasting

Boosting algorithm for ensemble learning

Simple boosting mechanism

Adaptive boosting algorithm

Gradient boosting

Extreme gradient boosting (XGBoost)

Stacking algorithm

Implementation of all strategy with the help of building implemented algorithms are available in Scikit-learn library

At the end of this course you will be able to apply ensemble learning technique on various different data set for regression and classification problem.

This course comes with 4+ hours of HD quality video plus quizzes to test your understanding about them ensemble learning.

Udemy always gives you 30 days money back guarantee. There is nothing to lose at your end. So what are you waiting for just enroll it now.

I will see you inside course.

Happy learning

Your regards

Ankit Mistry

Who this course is for:

Anyone has idea about machine learning and want improve model accuracy
Anyone who want to learn ensemble machine learning techniques

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