Decision Trees and Ensembling techinques in R studio. Bagging, Random Forest, GBM, AdaBoost & XGBoost in R programming

SUMMARY

This comprehensive course is tailored for individuals looking to master decision trees and their advanced variants, such as Random Forest, Gradient Boosting, and XGBoost, using R programming. It’s ideal for students and professionals aiming to solve business problems through machine learning without prior expertise in programming or data science.

Course Objectives

By the end of the course, participants will have a solid understanding of:

  • Decision Tree Models: Recognizing business scenarios suitable for decision tree applications and mastering the fundamentals of building these models.
  • Hyperparameter Tuning: Learning how to fine-tune the hyperparameters of decision tree models to optimize performance.
  • Predictive Analytics: Using decision trees for making accurate predictions and analyzing results effectively.
  • R Programming: Manipulating data and performing statistical computations with R, along with installation guidance for R Studio.

Course Structure

The course is divided into several key sections:

  1. Introduction to Machine Learning: Understanding core concepts and terminology related to machine learning, along with the steps involved in building a machine learning model.
  2. R Basics: Setting up R and R Studio, including performing fundamental operations necessary for data analysis.
  3. Data Preprocessing and Simple Decision Trees: Preparing data for analysis through techniques such as missing value imputation and variable transformation. Participants will also learn to create and visualize basic regression decision trees.
  4. Classification Trees: Expanding knowledge to classification decision trees, allowing participants to categorize data effectively.
  5. Ensemble Techniques: Delving into advanced methods like Random Forest, Bagging, AdaBoost, and XGBoost. These ensemble techniques enhance model stability and accuracy, crucial for robust machine learning applications.

Learning Outcomes

Participants will be equipped to identify relevant business problems and apply decision tree models to solve them. The course emphasizes not just running analyses but also preparing data and interpreting results to drive business decisions. By engaging with practical assignments, quizzes, and downloadable practice files, learners will gain hands-on experience alongside theoretical knowledge.

Instructor Credentials

The course is taught by experienced professionals Abhishek and Pukhraj, who have extensive backgrounds in global analytics consulting. Their practical insights into machine learning applications ensure a real-world understanding of the concepts taught.

Certification and Additional Resources

Upon course completion, students receive a verifiable certificate, enhancing their credentials in the field of machine learning. The course is designed to be interactive, with direct communication options for questions and support.

 

What you’ll learn
  • Solid understanding of decision trees, bagging, Random Forest and Boosting techniques in R studio
  • Understand the business scenarios where decision tree models are applicable
  • Tune decision tree model’s hyperparameters and evaluate its performance.
  • Use decision trees to make predictions
  • Use R programming language to manipulate data and make statistical computations.
  • Implementation of Gradient Boosting, AdaBoost and XGBoost in R programming language

Description

You’re looking for a complete Decision tree course that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in R, right?

After completing this course you will be able to:

  • Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost  of Machine Learning.
  • Have a clear understanding of Advanced Decision tree based algorithms such as Random Forest, Bagging, AdaBoost and XGBoost
  • Create a tree based (Decision tree, Random Forest, Bagging, AdaBoost and XGBoost) model in R and analyze its result.
  • Confidently practice, discuss and understand Machine Learning concepts

 

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.

 

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through Decision tree.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

 

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses – with over 150,000 enrollments and thousands of 5-star reviews like these ones:

 

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a decision tree based model, which are some of the most popular Machine Learning model, to solve business problems.

Below are the course contents of this course :

  • Section 1 – Introduction to Machine LearningIn this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.
  • Section 2 – R basicThis section will help you set up the R and R studio on your system and it’ll teach you how to perform some basic operations in R.
  • Section 3 – Pre-processing and Simple Decision treesIn this section you will learn what actions you need to take to prepare it for the analysis, these steps are very important for creating a meaningful.In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like  missing value imputation, variable transformation and Test-Train split. In the end we will create and plot a simple Regression decision tree.
  • Section 4 – Simple Classification TreeThis section we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python
  • Section 5, 6 and 7 – Ensemble technique
    In this section we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. In this course we will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.

 

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

What are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 3 parts:

Statistics and Probability – Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.

Understanding of Machine learning – Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Understanding of  models – Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

 

Why use R for Machine Learning?

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R

2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.

5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.

 

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

 

 

 

 

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