Decision Trees, Random Forests, AdaBoost and XGBoost in Python

Decision Trees, Random Forests, AdaBoost and XGBoost in Python

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Decision Trees, Random Forests, AdaBoost and XGBoost in Python
MP4 | Video: AVC 1280×720 | Audio: AAC 44KHz 2ch | Duration: 5 Hours | 2.45 GB
Genre: eLearning | Language: English

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 Tree- and tree-based advanced techniques course!
If you are a business manager, executive, or student and want to learn and apply Machine Learning to real-world business problems, this course will give you a solid base for that by teaching you some advanced machine learning techniques: Decision Trees, Random Forests, Bagging, AdaBoost, and XGBoost.
This course covers all the steps you should take while solving a business problem through Decision Trees. Most courses only focus on teaching you how to run an analysis, but we believe that what happens before and after running analyses is even more important: before running an analysis, it is very important that you have the right data and do some pre-processing on it. And after running an analysis, you should be able to judge how good your model is and interpret the results so it actually helps your business.
The course is taught by Abhishek and Pukhraj. As managers in the Global Analytics Consulting firm, we have helped businesses solve their business problems using Machine Learning techniques and we have used our experience to include 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.
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. 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 implement what you've learned practically. This course teaches you all the steps involved in creating Decision Tree-based models (some of the most popular Machine Learning models) to solve business problems.
All the code and supporting files for this course are available at –

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