Decision Trees, Random Forests, AdaBoost & XGBoost in Python

Short Description

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

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 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 – Python basicThis 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 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, 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.

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