Data Preparation &  Preprocessing

Part 1

1.Data Preprocessing Mastery

Learn normalization, scaling, and handling missing data techniques.

2.Data Quality Importance

Understand how data quality impacts model performance.

3.Data Augmentation Techniques

Explore methods to increase dataset diversity.

4.Labeled Dataset Significance

Grasp the importance of clean and labeled datasets in Generative AI.

5.Data Collection & Annotation

Familiarize yourself with data collection, annotation, and labeling processes.

6. Data Format Familiarization

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.

8.Data Visualization for Insights

Use visualization tools to gain insights into dataset characteristics.

9. Privacy Concerns

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.