Learn to use Driverless AI for data preparation.
Why take this course?
Master Data Preparation for H2O Driverless AI π
Course Overview:
Learn to use H2O’s powerful Driverless AI tool effectively for data preparation, a pivotal step in the machine learning pipeline. This comprehensive course is designed to empower you with the knowledge and skills needed to tackle real-world data challenges and enhance your predictive modeling capabilities. With H2O.ai University at your side, you’ll gain insights into best practices for data preparation that will set the foundation for successful AI model deployment.
Why Enroll in Data Prep for H2O Driverless AI?
- Expert Instruction: Led by Jonathan Farinella, Solutions Engineer at H2O, an expert in the field with a wealth of knowledge to share.
- Hands-On Learning: Gain practical experience with real-world datasets and see firsthand how to apply data preparation techniques using Driverless AI.
- Skill Mastery: From the basics to advanced concepts, this course is designed to solidify your understanding of data preparation for both tabular and time series data.
- Certification Pathway: This course is part of the H2O.ai University certification program, verifying your expertise to potential employers or clients.
Course Structure:
This course is meticulously structured into two main sections:
Section 1: Fundamentals of Data Preparation for Machine Learning π
- The Role of Data Quality: Learn why high-quality data is crucial for successful machine learning outcomes.
- Tabular Data and Machine Learning: Explore the importance of tabular datasets in classical machine learning, and understand how they differ from time series data.
- Supervised vs. Unsupervised Learning: Get to grips with the differences and common methods like classification and regression.
- Unit of Analysis and Dataset Construction: Understand why defining the unit of analysis is critical when constructing your datasets.
- Data Preparation Demonstrations in Driverless AI: Witness live demonstrations showcasing how to automate preprocessing tasks using Driverless AI, with the option to customize your workflow through Python code.
Section 2: Time Series Data Preparation and Best Practices β°
- Time Series Fundamentals: Delve into the key aspects of time series data, including the essential role of a date column and understanding autoregressive components.
- Handling Multiple Series in Datasets: Learn how to effectively handle datasets with multiple time series.
- Improving Model Performance: Receive best practices for preparing your datasets to improve the performance of predictive models.
- Customized Techniques in Driverless AI: Jonathan will guide you through dataset preparation, splitting techniques, and unique approaches tailored specifically for time series analysis using Driverless AI.
What You Will Learn:
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Understand the critical role data quality plays in your predictive modeling success.
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Master the art of preparing tabular data for machine learning tasks.
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Grasp the nuances and best practices for handling time series data.
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Develop customizable data preparation workflows within Driverless AI using Python code.
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Enhance your models’ performance by effectively preprocessing datasets.
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Ready to Dive Into Data Preparation with H2O Driverless AI? π
Join us in this enlightening journey and transform the way you approach data preparation. Whether you’re a data scientist, analyst, or simply eager to learn more about machine learning, this course will provide you with the tools and knowledge you need to succeed. Sign up today and take your first step towards mastering data preparation for H2O Driverless AI! π
Add-On Information:
- Understand the critical role of data quality in maximizing H2O Driverless AI’s performance, preventing “garbage in, garbage out” scenarios that undermine even advanced AutoML.
- Master diverse data ingestion strategies, learning to seamlessly connect Driverless AI to various data sources and efficiently load different file formats.
- Perform targeted Exploratory Data Analysis (EDA) to proactively identify and diagnose data issues that specifically impact Driverless AI’s model building and feature engineering.
- Implement effective missing value imputation strategies, understanding the nuances of different methods and their implications on Driverless AI’s subsequent learning.
- Develop robust techniques to detect and treat outliers and anomalies, preventing them from skewing Driverless AI’s models and ensuring more reliable predictions.
- Craft powerful feature engineering, both manual and in understanding how to leverage and augment Driverless AI’s automated capabilities for richer, more predictive features.
- Apply essential data transformation techniques including scaling, normalization, and advanced categorical encoding, preparing diverse data types optimally for Driverless AI.
- Address imbalanced datasets using techniques like over/undersampling, synthetic data generation, and class weighting to ensure fair and accurate models from Driverless AI.
- Prepare complex time series data by creating lags, rolling statistics, and extracting temporal features, empowering Driverless AI for accurate forecasting and sequence analysis.
- Effectively preprocess text data, covering tokenization, stemming, lemmatization, and basic vectorization, to unlock insights from unstructured data within Driverless AI.
- Implement rigorous data validation and quality checks throughout your pipeline, ensuring data integrity, consistency, and reliability before feeding it to H2O Driverless AI.
- Learn to identify and mitigate data leakage, designing your preparation workflow to prevent misleadingly optimistic model performance and ensure true generalizability from Driverless AI.
- Build reproducible data preparation pipelines for consistency, efficiency, and seamless integration with H2O Driverless AI workflows, supporting continuous model development.
PROS:
- Maximize Model Performance: Directly enhance the accuracy, robustness, and interpretability of models built with H2O Driverless AI.
- Build Trustworthy AI: Ensure your automated machine learning outcomes are based on clean, validated data, leading to more reliable and ethical AI solutions.
- Accelerate Development: Drastically reduce iterative debugging and rework by providing Driverless AI with high-quality, pre-processed data from the outset.
- Deepen Data Understanding: Gain a profound understanding of your datasets, identifying critical nuances and potential pitfalls for any machine learning project.
CONS:
- Initial Time Investment: Thorough data preparation can be a significant time commitment, requiring careful planning and execution before model training.
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