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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