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.

Course access

H2O.ai Certificate H2O.ai Certificate
 headshot

Jonathan Farinela, Solutions Engineer

Statistician with over a decade of experience in analytics and data science, primarily working in Research and Development, also has experience with Demand Forecast for retail and CRM for Financial Services. Last 5+ years focusing on helping customers during pre and post sales steps, leading and conducting Proof of Values (POV) with AI and ML projects, educating, enabling, and driving AI solutions for business problems, from data to value always aiming ROI and financial impact for the business.