Data Preparation &
Preprocessing
Part 1
1.Data Preprocessing Mastery
Learn normalization, scaling, and handling missing data techniques.
Learn normalization, scaling, and handling missing data techniques.
2.Data Quality Importance
Understand how data quality impacts model performance.
Understand how data quality impacts model performance.
3.Data Augmentation Techniques
Explore methods to increase dataset diversity.
Explore methods to increase dataset diversity.
4.Labeled Dataset Significance
Grasp the importance of clean and labeled datasets in Generative AI.
Grasp the importance of clean and labeled datasets in Generative AI.
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5.Data Collection & Annotation
Familiarize yourself with data collection, annotation, and labeling processes.
Familiarize yourself with data collection, annotation, and labeling processes.
6. Data Format Familiarization
Experiment with various data formats and their implications on model training.
Experiment with various data formats and their implications on model training.
7.Unstructured Data Handling
Learn techniques for handling unstructured data like images, audio, and text.
Learn techniques for handling unstructured data like images, audio, and text.
8.Data Visualization for Insights
Use visualization tools to gain insights into dataset characteristics.
Use visualization tools to gain insights into dataset characteristics.
9. Privacy Concerns
Be mindful of privacy and ethical considerations when handling sensitive data.
Be mindful of privacy and ethical considerations when handling sensitive data.
10.Augmentation for Limited Data
Implement data augmentation techniques to enhance model training with limited data.
Implement data augmentation techniques to enhance model training with limited data.
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