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
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?
You’ve found the right Decision Trees and tree based advanced techniques course!
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.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Decision tree, Random Forest, Bagging, AdaBoost and XGBoost.
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:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
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 Learning
In 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 basic
This 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 trees
In 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 Tree
This 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.
By the end of this course, your confidence in creating a Decision tree model in R will soar. You’ll have a thorough understanding of how to use Decision tree modelling to create predictive models and solve business problems.
Decision Trees, Random Forests, Bagging & XGBoost: R Studio
Decision Trees and Ensembling techinques in R studio. Bagging, Random Forest, GBM, AdaBoost & XGBoost in R programming