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Understanding the Python Decision Tree: Machine Learning Basics

Building decision trees in Python explained by industry professionals
Course from BitDegree
 9 students enrolled
What a decision tree is and what it’s used for
How to use decision trees to make predictions
In what business scenarios decision trees are applicable
What machine learning model’s hyperparameters are and how to evaluate its performance
What advantages and disadvantages of the different algorithms are

Creating a decision tree in Python is a topic that raises a lot of questions for a beginner. What exactly is it, and what do we use it for? Where do we start building one, and what first steps do we take? Why do we use Python? Let’s begin at the top.

Simply put, a Python decision tree is a machine-learning method that we use for classification. A data scientist creates a model and feeds it some data. The model then extracts and learns some decision rules, which allows it to make predictions. In this 5+ hours course, industry professionals Abhishek and Pukhraj will explain how you can build a decision tree using Python: all you need to do is let them guide you!

Building decision trees in Python: what you’ll learn

We will start the course by getting familiar with the fundamentals of machine learning. In its essence, it’s a part of computer sciences that focuses on teaching computers and improving independent artificial intelligence (AI). You will see examples and definitions and get to know the steps necessary for building a model. Then, we will review the basics of Python and get to know a few of its libraries.

Having finished with all the fundamentals, we will move to the Python decision tree itself. The instructors will explain all the underlying theory and how to prepare for the analysis. Upon getting familiar with the concept, you will get a chance to practice, too! You will be building two types of a Python decision tree: regression and classification.

When you get the basics down, the lectures will move onto more complex topics. You will find out how to use advanced ensemble techniques (such as Random Forest, Baggind, Gradient Boosting, AdaBoost, and XGBoost) when creating a decision tree in Python.

Why do we use Python?

When building decision trees in Python, beginners sometimes ask why this programming language has been selected as our primary tool – after all, there are dozens of them to choose from! The idea is simple: in the last few years, Python has experienced incredible growth in popularity, and now two out of every three data scientists name it as the most critical tool for machine learning and analytics.

As the trend is very unlikely to diminish soon, knowing how to create a Python decision tree is a great skill to acquire for every aspiring data scientist. It is also a general-purpose language that opens a lot of doors for a flexible and enthusiastic developer.

Building a decision tree using Python: a few final words

A Python decision tree has multiple advantages. It does not require a ton of preparation but can handle numerical, categorical, and multi-output data. Most also find them easy to interpret and comprehend. Take the course presented by Abhishek and Pukhraj today and learn how you can use Python to build decision trees today – after all, machine learning is the future, and you want to be a part of it!

Understanding the Python Decision Tree: Machine Learning Basics
$ 4.99
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