This 90-minute guided-project, "Pyspark for Data Science: Customer Churn Prediction," is a comprehensive guided-project that teaches you how to use PySpark to build a machine learning model for predicting customer churn in a Telecommunications company. This guided-project covers a range of essential tasks, including data loading, exploratory data analysis, data preprocessing, … [Read more...] about Machine Learning with PySpark: Customer Churn Analysis
Data Science
Trees, SVM and Unsupervised Learning
"Trees, SVM and Unsupervised Learning" is designed to provide working professionals with a solid foundation in support vector machines, neural networks, decision trees, and XG boost. Through in-depth instruction and practical hands-on experience, you will learn how to build powerful predictive models using these techniques and understand the advantages and disadvantages of … [Read more...] about Trees, SVM and Unsupervised Learning
BigQuery Soccer Data Ingestion
This is a self-paced lab that takes place in the Google Cloud console. Get started with sports data science by importing soccer data on matches, teams, players, and match events into BigQuery tables. Information access uses multiple formats, and BigQuery makes working with multiple data sources simple. In this lab you will get started with sports data science by importing … [Read more...] about BigQuery Soccer Data Ingestion
Mastering Data Analysis with Pandas: Learning Path Part 5
In this structured series of hands-on guided projects, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the fifth of a series of multiple guided projects (learning path) that is designed for anyone who wants … [Read more...] about Mastering Data Analysis with Pandas: Learning Path Part 5
Handling Imbalanced Data Classification Problems
In this 2-hour long project-based course on handling imbalanced data classification problems, you will learn to understand the business problem related we are trying to solve and and understand the dataset. You will also learn how to select best evaluation metric for imbalanced datasets and data resampling techniques like undersampling, oversampling and SMOTE before we use them … [Read more...] about Handling Imbalanced Data Classification Problems
