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:

1. A supervised regression problem where students predict Airbnb listing prices.

2. A binary classification problem where students work with actual logs of website visits to predict online conversions.

3. A multi-class classification problem where we would predict the credit rating of customers in 3 categories

4. A multi-class text classification problem where students work with large datasets and more than 200 classes.

5. 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

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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