Decision Trees, Random Forests, AdaBoost & XGBoost in Python – Start-Tech Academy

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

Looking for a comprehensive Decision Tree course that covers everything you need to know about creating Decision Tree/Random Forest/XGBoost models in Python? Look no further! This course teaches you how to identify business problems that can be solved using machine learning techniques, and provides a clear understanding of advanced decision tree-based algorithms such as Random Forest, Bagging, AdaBoost, and XGBoost.

By the end of the course, you’ll be able to confidently create and analyze tree-based models in Python, and discuss and apply machine learning concepts to real-world business problems.

What sets this course apart is that it goes beyond just teaching you how to run analysis. It emphasizes the importance of data preprocessing and interpreting results to help businesses make informed decisions. The instructors, Abhishek and Pukhraj, are experienced managers in global analytics consulting firms who have used their expertise to create practical and effective course content.

With over 150,000 enrollments and thousands of 5-star reviews, this course is a top choice for anyone looking to learn and apply machine learning techniques. You’ll have access to practice files, quizzes, and assignments to reinforce your learning, and the instructors are always available to answer questions and provide guidance.

The course covers everything from the basics of machine learning and Python to advanced techniques like ensemble learning, and is ideal for business managers, executives, and students looking to improve their machine learning skills. By the end of the course, you’ll have the skills and confidence to create powerful decision tree models that solve real-world business problems.

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.

What you’ll learn in Decision Trees, Random Forests, AdaBoost & XGBoost in Python

• 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

Who this course is for:

• People pursuing a career in data science
• Working Professionals beginning their Data journey
• Statisticians needing more practical experience
• Anyone curious to master Decision Tree technique from Beginner to Advanced in short span of time

About Start-Tech Academy

Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.

Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey.

Founded by Abhishek Bansal and Pukhraj Parikh.

Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.

Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.

Salepage: https://www.udemy.com/course/machine-learning-advanced-decision-trees-in-python/