Practical Data Science with Amazon SageMaker

In this intermediate course, delve into the practical application of machine learning (ML) using Amazon SageMaker, aimed at addressing a tangible use case: analyzing customer retention to shape customer loyalty initiatives. We’ll guide you step-by-step through the typical stages of a data science ML workflow, encompassing dataset analysis, visualization, data preparation, and feature engineering.
Key Takeaways:
• Techniques to ready a dataset for model training.
• Strategies to train and assess a machine learning model’s performance.
• Methods for auto-tuning a machine learning model for optimal results.
• Preparing and streamlining your machine learning model for real-world deployment.
• And many more insightful practices.
This course is crafted for:
• Developers looking to integrate ML into their solutions.
• Data scientists keen on leveraging Amazon SageMaker for practical ML deployment.