Learn essential data preparation techniques for tabular and time series data using H2O Driverless AI. Understand data quality, build datasets, and customize preprocessing with Python.
Ideal for those looking to enhance their machine learning projects with Driverless AI.
What you'll learn
- Data Quality for Machine Learning
Understand why clean and well-structured data is critical for model success.
- Tabular Data Preparation
Learn the basics of supervised and unsupervised learning, tabular formats, and unit of analysis.
- Custom Preprocessing in Driverless AI
See how to prepare datasets, automate tasks, and use Python code for custom preprocessing.
- Time Series Data Basics
Get familiar with time series concepts like date columns, autoregressive models, and multiple series handling.
- Dataset Splitting Techniques
Learn practical strategies to split and prepare time series datasets for model training..
- Best Practices in Data Prep
Understand common challenges and apply solutions to improve model performance.
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