XGBoost Deep Dive w/ Python & Pandas | Hands-on Data Science
XGBoost Deep Dive w/ Python & Pandas | Hands-on Data Science, available at $89.99, has an average rating of 4, with 41 lectures, based on 12 reviews, and has 93 subscribers.
You will learn about 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 This course is ideal for individuals who are Python Developers with some experience working with data or Data Analysts that want to transition to Data Science or a Machine Learning Engineer Role or Developers with some python experience that want to learn some machine learning with real world projects or Data Scientists that want to learn more about XGBoost from a practical, applied standpoint or University students that want to get some Hands-On experience with XGBoost It is particularly useful for Python Developers with some experience working with data or Data Analysts that want to transition to Data Science or a Machine Learning Engineer Role or Developers with some python experience that want to learn some machine learning with real world projects or Data Scientists that want to learn more about XGBoost from a practical, applied standpoint or University students that want to get some Hands-On experience with XGBoost.
Enroll now: XGBoost Deep Dive w/ Python & Pandas | Hands-on Data Science
Summary
Title: XGBoost Deep Dive w/ Python & Pandas | Hands-on Data Science
Price: $89.99
Average Rating: 4
Number of Lectures: 41
Number of Published Lectures: 41
Number of Curriculum Items: 41
Number of Published Curriculum Objects: 41
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
The XGBoost Deep Dive course is a comprehensive program that teaches students the top skills they need to become a Python machine learning engineer or data scientist. The course focuses on using the Python version of 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 (Sentence Transformers) and TF-IDF features.
The course includes five hands-on projects with Python:
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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
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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, Pandas and Python and will be able to apply these skills to their own machine learning and data science projects.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Why XGBoost?
Lecture 2: XGBoost Intuition
Lecture 3: Set up work environment
Lecture 4: Configure Anaconda Python Environment
Lecture 5: Feedback to improve the course!
Chapter 2: Supervised Regression – Predict AirBnB Listing Prices with XGBoost
Lecture 1: Supervised Regression Project
Lecture 2: Section Materials
Lecture 3: Dataset Overview
Lecture 4: Target Variable Definition
Lecture 5: List Categorical and Numeric Features
Lecture 6: Numeric Feature Engineering with Pandas
Lecture 7: Feature Engineering Categorical Variables
Lecture 8: Feature Engineering Date Features
Lecture 9: Code Clean-Up
Lecture 10: Preprocess data with Pandas
Lecture 11: Train/Test Split and Missing Values
Lecture 12: One Hot Encoding
Lecture 13: XGBoost Parameter Tuning and Model Training
Chapter 3: Binary Classification Project
Lecture 1: Introduction
Lecture 2: Section Materials
Lecture 3: Data Preprocessing with Pandas
Lecture 4: Feature Engineering with Pandas
Lecture 5: Manual Hyperparameter Tuning with XGBoost
Lecture 6: Feature Importance and Cross Validation with XGBoost
Lecture 7: Model Evaluation with AUC
Lecture 8: Precision, Recall and F1-Scoreand Probability Cut-Offs
Lecture 9: Choosing ONE Probability Cut-Offs
Lecture 10: Automated Hyperparameter Tuning
Chapter 4: Multi-Class Classification – Credit Score Classification
Lecture 1: Multi Class Classification Project
Lecture 2: Section Materials
Lecture 3: Multi-Class Classification – Dataset Overview
Lecture 4: Feature Engineering with Pandas
Lecture 5: Feature Engineering with Pandas – 2
Lecture 6: Train XGBoost Model – Credit Score Prediction
Chapter 5: Text Multi-Class Classification
Lecture 1: Text Multi-Class Classification Project
Lecture 2: Section Materials
Lecture 3: Data Preprocessing for Text Classification
Lecture 4: Feature Engineering and Model Training
Chapter 6: Time Series Forecasting with XGBoost
Lecture 1: Time Series Forecasting with XGBoost
Lecture 2: Section Materials
Lecture 3: Time Series Forecasting – Daily Data
Instructors
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Martin Bel
Data Scientist
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 3 votes
- 3 stars: 1 votes
- 4 stars: 3 votes
- 5 stars: 5 votes
Frequently Asked Questions
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