Explore a comprehensive tutorial on machine learning using the DataRobot AI Cloud Platform. Learn about data preparation, AutoML, VisualML, and MLOps while focusing on the model building process. Dive into in-depth details of AI cloud model training, evaluation, performance, re-training, validation, and various other steps. Discover how to import datasets, perform exploratory data analysis, use supervised ML techniques, and explore advanced training options. Examine data quality, feature associations, and AI model repositories. Understand bias and fairness in AI, feature impact and effect, prediction explanations, and model evaluation techniques. Learn about advanced model tuning, comparisons, and ways to improve model accuracy through ensembling and blending. Finally, explore model deployment from the ML pipeline, AI report generation, and platform documentation.
Overview
Syllabus
- Video Start
- Video Content Intro
- AI cloud platform access
- Data Preparation Tutorial Intro
- ML Development Project
- Importing Dataset for ML
- ML Focussed EDA with source data
- Supervised ML with AI Platform
- Advance Options with ML Training
- ML Training Start
- Data Quality Exploration
- Features List in Source Data
- Features Association
- Data Quality Assessment
- AI Models in ML Project
- AI Models Repo
- Bias and Fairness
- Feature Impact and Feature Effect
- Prediction Explanations
- Explore Model Details
- Model Evaluations
- Advanced Model Tuning
- Model comparisons
- Model Speed vs Model Accuracy
- Model Insight
- Improving Model Accuracy
- Ensembling or Model Blending
- Deploy Model from ML Pipeline
- AI Report Generation
- AI Platform Documentation
- Thanks
- Credits
Taught by
Prodramp
