XGBoost Deep Dive! Hands on Machine learning & Data Science

Learn the top skill to become a Machine Learning Eeer or Data Scientist

Learn XGBoost, the best and most popular algorithm for tabular data

Leverage Pandas for Feature Eeering and data Visualization

Understand how to define a machine learning project, going from raw data to a trained model

Learn Gradient Boosting Decision Trees working with realistic datasets and Hands on projects

Learn to apply XGBoost to NLP problems using Deep Learning and TF-IDF features

Project 1: Supervised Regression problem where we predict AirBnB listings prices

Project 2: Binary Classification problem where we work with actual logs of a website visits to predict online conversions

Project 3: Multi Class text Classification. We work with large datasets and more than 200 classes

Project 4: series Forecasting with XGBoost

Some Python and experience

Some familiarity with Jupyter Notebooks

Some pandas experience is ideal but I explain everything I do line by line

The XGBoost Deep Dive course is a comprehensive program that teaches students the top skills they need to become a machine learning eeer or data scientist. The course focuses on XGBoost, the best and most popular algorithm for tabular data, and teaches students how to use it effectively for a variety of machine learning tasks.Throughout the course, students will learn how to leverage Pandas for feature eeering and data visualization, and will understand how to define a machine learning project, going from raw data to a trained model. They will also learn about gradient boosting decision trees and will work with realistic datasets and hands-on projects to apply their knowledge in a practical setting.In addition, students will learn how to apply XGBoost to Natural Language Processing (NLP) problems using deep learning and TF-IDF features.The course includes five projects:A supervised regression problem where students predict Airbnb listing prices.A binary classification problem where students work with actual logs of website visits to predict online conversions.A multi-class classification problem where we would predict the credit rating of customers in 3 categoriesA multi-class text classification problem where students work with large datasets and more than 200 classes.A series forecasting problem where students use XGBoost to make predictions.By the end of the course, students will have a strong understanding of how to use XGBoost and will be able to apply these skills to their own machine learning and data science projects.

Python Developers with some experience working with data

Data Analysts that want to transition to Data Science or a Machine Learning Eeer Role

Developers with some python experience that want to learn some machine learning with real world projects

Data Scientists that want to learn more about XGBoost from a practical, applied standpoint

University students that want to get some Hands-On experience with XGBoost

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