Data & Analytics


Decision Trees, Random Forests, AdaBoost & XGBoost in Python

Decision Trees and Ensembling techniques in Python. How to run Bagging, Random Forest, GBM, AdaBoost & XGBoost in Python

4.17 (490 reviews)

Decision Trees, Random Forests, AdaBoost & XGBoost in Python



7 hours


Nov 2020

Last Update
Regular Price

What you will learn

Get a solid understanding of decision tree

Understand the business scenarios where decision tree is applicable

Tune a machine learning model's hyperparameters and evaluate its performance.

Use Pandas DataFrames to manipulate data and make statistical computations.

Use decision trees to make predictions

Learn the advantage and disadvantages of the different algorithms


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 Python, 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 Python 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

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 on Linear Regression:

  • 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 - Python basic

    This section gets you started with Python.

    This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

  • 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, Baggind, Gradient Boosting, AdaBoost and XGBoost.

By the end of this course, your confidence in creating a Decision tree model in Python will soar. You'll have a thorough understanding of how to use Decision tree  modelling to create predictive models and solve business problems.

Go ahead and click the enroll button, and I'll see you in lesson 1!


Start-Tech Academy


Below is a list of popular FAQs of students who want to start their Machine learning journey-

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 4 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

Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python

Understanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Why use Python for data Machine Learning?

Understanding Python is one of the valuable skills needed for a career in Machine Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

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.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.



Welcome to the Course!

Course Resources

Setting up Python and Python Crash Course

Installing Python and Anaconda

Opening Jupyter Notebook

Introduction to Jupyter

Arithmetic operators in Python: Python Basics

Strings in Python: Python Basics

Lists, Tuples and Directories: Python Basics

Working with Numpy Library of Python

Working with Pandas Library of Python

Working with Seaborn Library of Python

Machine Learning Basics

Introduction to Machine Learning

Building a Machine Learning Model

Simple Decision trees

Basics of decision trees

Understanding a Regression Tree

The stopping criteria for controlling tree growth

The Data set for the Course

Importing Data in Python

Missing value treatment in Python

Dummy Variable creation in Python

Dependent- Independent Data split in Python

Test-Train split in Python

Creating Decision tree in Python

Evaluating model performance in Python

Plotting decision tree in Python

Pruning a tree

Pruning a tree in Python

Simple Classification Tree

Classification tree

The Data set for Classification problem

Classification tree in Python : Preprocessing

Classification tree in Python : Training

Advantages and Disadvantages of Decision Trees

Ensemble technique 1 - Bagging

Ensemble technique 1 - Bagging

Ensemble technique 1 - Bagging in Python

Ensemble technique 2 - Random Forests

Ensemble technique 2 - Random Forests

Ensemble technique 2 - Random Forests in Python

Using Grid Search in Python

Ensemble technique 3 - Boosting



Ensemble technique 3a - Boosting in Python

Ensemble technique 3b - AdaBoost in Python

Ensemble technique 3c - XGBoost in Python


Add-on 1: Preprocessing and Preparing Data before making ML model

Gathering Business Knowledge

Data Exploration

The Dataset and the Data Dictionary

Importing Data in Python

Univariate analysis and EDD

EDD in Python

Outlier Treatment

Outlier Treatment in Python

Missing Value Imputation

Missing Value Imputation in Python

Seasonality in Data

Bi-variate analysis and Variable transformation

Variable transformation and deletion in Python

Non-usable variables

Dummy variable creation: Handling qualitative data

Dummy variable creation in Python

Correlation Analysis

Correlation Analysis in Python



Bonus Lecture


Utkrist12 December 2020

Instructor is not engaging. it is such an interesting topic but the instructor slow delivery is pulling the life out of it. The presentation that we are looking at, the written martial is complicated and not at all easy to understand. the presentation could have been a bit easy and more understandable and interesting. Conclusively : this course is Difficult to understand.

Simone5 September 2020

There is also some materials regarding the preprocessing phase that a lot of other higher rated courses don't touch upon. Interesting part also on how to treat seasonality. Plus good explanation of all the techniques defined by the course's title.

Hoang31 August 2020

Your lessons is useful and inspired but sometimes I can not get your idea because I am not familiar with your English accent and the automation sub is quite bad :(

Maheswarasarma23 August 2020

jupyter notebook cursor is too large and a black squared box is blocking to view the text behind the cursor

Omkar16 July 2020

Teaching is very Good.All concept explained very well with the help of e.g. I will recommender everyone who has interested in machine learing

Jerome15 July 2020

Les sous-titres sont très mauvais, on dirait une traduction à la volée sur un très accent du formateur : du coup, la plupart des sous-titres ne veulent rien dire. Pourtant le sujet est intéressant !

Anish28 May 2020

Its really good, and yes its a good match for me because i wanna publish a paper on ensembles methods.

Ajay12 May 2020

Overall good course, only missed some related case study which uses decision trees to solve their problems

Rodolfo_Rivas3 May 2020

Step by step walk through of a quasi real life analysis. Very straight forward to learn how to code different tools. Some concepts are only introduced. For example it would help a slide to explain confusion matrix or how ensemble techniques get to a final score.

Ammar28 April 2020

It was amazing and really informative course and an excellent instructor, my thanks for both of them and thank you Udemy.

Mikhail3 December 2019

The information in the couse wasn't sufficient. The absence of hometasks makes the course a kind of a lecture

Bishal1 September 2019

The Course provides all the details we need for Decision Trees techniques. It is clearly explained. Thank you so much.

Michael21 August 2019

The course is extremely light on mathematics and theory. It's a very good introductory course, and covers all the basic stuff.


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