Ensemble Machine Learning in Python : Adaboost, XGBoost
- 4.4
-
![]()
![]()
![]()
![]()
Brief Introduction
Ensemble Machine Learning technique like Voting, Bagging, Boosting, Stacking, Adaboost, XGBoost in Python Sci-kit LearnDescription
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
Requirements
- Requirements
- Basics of Python programming
- Knowledge about Machine learning algorithms
Ankit Mistry
4.4 Raiting
Ensemble Machine Learning in Python: Random Forest, AdaBoost
- 4.6
-
![]()
![]()
![]()
![]()
![]()
Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning....
Decision Trees, Random Forests, AdaBoost & XGBoost in Python
- 4.2
-
![]()
![]()
![]()
![]()
![]()
Decision Trees and Ensembling techniques in Python. How to run Bagging, Random Forest, GBM, AdaBoost & XGBoost in Python After completing this course you will be able to:...
Machine Learning with Python from Scratch
- 3.8
-
![]()
![]()
![]()
![]()
![]()
Mastering Machine Learning Algorithms including Neural Networks with Numpy, Pandas, Matplotlib, Seaborn and Scikit-Learn But how do you get started? But what exactly is Machine Learning?...
Ensemble Machine Learning in Python : Adaboost, XGBoost
- 4.4
-
![]()
![]()
![]()
![]()
![]()
Ensemble Machine Learning technique like Voting, Bagging, Boosting, Stacking, Adaboost, XGBoost in Python Sci-kit Learn Do you think that you can depend on a just one person advice....
