XGBoost Deep Dive! Hands on Machine learning & Data Science

XGBoost, Pandas, Feature Engineering, Machine Learning, Data Science, Python, deep learning, NLP,Time Series Forecasting
What you'll learn
Learn the top skill to become a Machine Learning Engineer or Data Scientist
Learn XGBoost, the best and most popular algorithm for tabular data
Leverage Pandas for Feature Engineering 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: Time series Forecasting with XGBoost
Requirements
Some Python and experience
Some familiarity with Jupyter Notebooks
Some pandas experience is ideal but I explain everything I do line by line
Description
The XGBoost Deep Dive course is a comprehensive program that teaches students the top skills they need to become a machine learning engineer 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 engineering 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 categories
A multi-class text classification problem where students work with large datasets and more than 200 classes.
A time 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.

Who this course is for:
Python Developers with some experience working with data
Data Analysts that want to transition to Data Science or a Machine Learning Engineer 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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