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Free Download How To Clean and Prep Your Corporate Data Before Fine Tuning
Published 10/2023
Created by Richard Aragon
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 8 Lectures ( 30m ) | Size: 627 MB
Everything A Business Nees To Prep Their Data Before Fine Tuning An AI Model
What you'll 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.
Requirements
This course assumes a basic understanding of Machine Learning and a basic understanding of Python and/or SQL
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 inconsistenciesStandardize your data formatting using SQL and PythonHandle missing values and outliers in PythonPerform feature engineering to improve your model's performanceApply the above techniques to a real-world example of customer churn predictionThis 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.PrerequisitesBasic knowledge of SQL and Python is recommendedCourse MaterialsVideo lecturesCode snippetsExercises Course StructureModule 1: Introduction to Data Cleaning and PreparationModule 2: Identifying and Removing Data Errors and InconsistenciesModule 3: Standardizing Data FormattingModule 4: Handling Missing Values and OutliersModule 5: Performing Feature EngineeringModule 6: Real-World Example: Customer Churn PredictionConclusionThis 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.
Who this course is for
Intermediate Developers and Above Who Are Looking For a Data Preparation Course
Homepage
Code:
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