How To Clean and Prep Your Corporate Data Before Fine Tuning
Everything A Business Nees To Prep Their Data Before Fine Tuning An AI Model
Description
This course will teach you how to clean and prep your corporate data before fine tuning. You will learn how to identify and remove common data errors and inconsistencies, standardize your data formatting, handle missing values and outliers, and perform feature engineering to improve your model's performance.
You will also learn how to apply these techniques to a real-world example of customer churn prediction.
By the end of this course, you will be able to:
Identify and remove common data errors and inconsistencies
Standardize your data formatting using SQL and Python
Handle missing values and outliers in Python
Perform feature engineering to improve your model's performance
Apply the above techniques to a real-world example of customer churn prediction
This course is designed for anyone who wants to learn how to clean and prep their corporate data for fine tuning, including data scientists, machine learning engineers, and business analysts.
Prerequisites
Basic knowledge of SQL and Python is recommended
Course Materials
Video lectures
Code snippets
Exercises
Course Structure
Module 1: Introduction to Data Cleaning and Preparation
Module 2: Identifying and Removing Data Errors and Inconsistencies
Module 3: Standardizing Data Formatting
Module 4: Handling Missing Values and Outliers
Module 5: Performing Feature Engineering
Module 6: Real-World Example: Customer Churn Prediction
Conclusion
This course will teach you the essential skills you need to clean and prep your corporate data for fine tuning. By taking this course, you will be able to improve the performance of your machine learning models and get more value from your data.
What You Will Learn!
- How to identify and remove common data errors and inconsistencies, including duplicates, incorrect or missing values, inconsistent formatting, and outliers.
- How to standardize your data formatting using SQL and Python.
- How to handle missing values and outliers in Python.
- How to perform feature engineering to improve your model's performance.
- How to apply the above techniques to a real-world example of customer churn prediction.
Who Should Attend!
- Intermediate Developers and Above Who Are Looking For a Data Preparation Course