Description

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.

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.

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

Requirements

• Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same

What Will I Get ?

• 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