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Data Preparation for Machine Learning
4.48
Beginner level
1.5 learning hrs
Explore how to prepare data for machine learning in this focused course. Learn techniques for cleaning, transforming, and organizing data to enhance your models' accuracy.
Modules updated 07/2025
About this course
In the free "Preparing Data for Machine Learning" course, participants will delve into crucial techniques for optimizing machine learning models. This comprehensive course covers key topics including preventing Data Leakage, which ensures that the model training process is robust and free from unintentional biases.
Participants will also learn to build efficient pipelines to automate data preparation workflows, enhancing productivity and consistency. The module on k-fold Cross Validation introduces a reliable method for evaluating model performance using different subsets of data.
Additionally, the course addresses Data Balancing Techniques, vital for training models on datasets that accurately reflect diverse scenarios. This course is meticulously designed to equip aspiring data scientists with the skills needed to prepare data effectively, paving the way for advanced machine learning applications.
Course outline
Case Study
This module introduces you to the case study, where you will get an opportunity to apply your theoretical knowledge of Measures of Central Tendency to a practical scenario.
Agenda For Preparing Data For Machine Learning
Data Leakage
Building Pipelines
k-fold Cross Validation
Data Balancing Techniques
Get access to the complete curriculum once you enroll in the course
4.48
What our learners enjoyed the most
Skill & tools
63% of learners found all the desired skills & tools
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