Ensemble Machine Learning in Python: Random Forest, AdaBoost

BY
Udemy

Looking for the right course on Ensemble Methods of Machine Learning? Enroll in Udemy’s online short certificate course.

Mode

Online

Fees

₹ 1199

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course overview

Ensemble Machine Learning in Python: Random Forest, AdaBoost course is an online course that could be taken by learners who are keen to learn the Ensemble Machine Learning. The curriculum will explore different Ensemble Methods of Python, namely,  Boosting, Bagging, Bootstrap, and Statistical Machine Learning for Data Science. The course is relevant as Machine Learning is embedded in all walks of life and all industries. 

Ensemble Machine Learning in Python: Random Forest, AdaBoost online course, provided by Udemy, trains the learners in ensemble methods of Machine learning which is to combine the available models of machine learning and come with a powerful model outdoing the shortcomings. The participants will go through ways to bring together models like decision trees and logistic regression to build more outstanding models. The programme specifically focuses on studying the Random Forest and AdaBoost algorithms. 

Ensemble Machine Learning in Python: Random Forest, AdaBoost certification demands certain prerequisites including Calculus, Probability, Object-oriented programming, Numpy coding, Python coding and Simple machine learning models. The short training also enables the participants to engage in hands-on practical aspects through many experiments to use these algorithms on real datasets. The interested learners could enrol on the course by paying the fee. Early birds will be given a discount. 

The highlights

  • Online course 
  • Free materials 
  • English videos with subtitle
  • Shareable certificate
  • Full lifetime access
  • Access on mobile and TV
  • 30-Day Money-Back Guarantee

Program offerings

  • Full lifetime access
  • Access on mobile and tv
  • Certificate of completion
  • Flexible deadlines
  • 5.5 hours on-demand video
  • English videos subtitle

Course and certificate fees

Fees information
₹ 1,199
certificate availability

Yes

certificate providing authority

Udemy

Who it is for

What you will learn

Knowledge of python

At the end of the Ensemble Machine Learning in Python: Random Forest, AdaBoost online certification, the participating learners will be able to understand the bias-variance decomposition, the method of bootstrap and its application to bagging and the role of bagging in improving the classification and regression performance. Plus, the learner will be able to gain knowledge on Random Forest and its application.

The syllabus

Get Started

  • Outline and Motivation
  • Where to get the Code and Data
  • All Data is the Same
  • Plug-and-Play
  • How to Succeed in This Course

Bias-Variance Trade-Off

  • Bias-Variance Key Terms
  • Bias-Variance Trade-Off
  • Bias-Variance Decomposition
  • Polynomial Regression Demo
  • K-Nearest Neighbor and Decision Tree Demo
  • Cross-Validation as a Method for Optimizing Model Complexity
  • Suggestion Box

Bootstrap Estimates and Bagging

  • Bootstrap Estimation
  • Bootstrap Demo
  • Bagging
  • Bagging Regression Trees
  • Bagging Classification Trees
  • Stacking

Random Forest

  • Random Forest Algorithm
  • Random Forest Regressor
  • Random Forest Classifier
  • Random Forest vs Bagging Trees
  • Implementing a "Not as Random" Forest
  • Connection to Deep Learning: Dropout

AdaBoost

  • AdaBoost Algorithm
  • Additive Modeling
  • AdaBoost Loss Function: Exponential Loss
  • AdaBoost Implementation
  • Comparison to Stacking
  • Connection to Deep Learning
  • Summary and What's Next

Background Review

  • Confidence Intervals

Setting Up Your Environment (FAQ by Student Request)

  • Anaconda Environment Setup
  • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help With Python Coding for Beginners (FAQ by Student Request)

  • How to Code by Yourself (part 1)
  • How to Code by Yourself (part 2)
  • Proof that using Jupyter Notebook is the same as not using it
  • Python 2 vs Python 3

Effective Learning Strategies for Machine Learning (FAQ by Student Request)

  • How to Succeed in this Course (Long Version)
  • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
  • Machine Learning and AI Prerequisite Roadmap (pt 1)
  • Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix / FAQ Finale

  • What is the Appendix?
  • BONUS

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