From the course: AWS Certified Machine Learning Engineer Associate (MLA-C01) Cert Prep

Unlock this course with a free trial

Join today to access over 24,900 courses taught by industry experts.

Intro: Modelling (SageMaker built-in algorithms)

Intro: Modelling (SageMaker built-in algorithms)

From the course: AWS Certified Machine Learning Engineer Associate (MLA-C01) Cert Prep

Start my 1-month free trial Buy for my team

Intro: Modelling (SageMaker built-in algorithms)

“

- [Lecturer] Hello guys, and welcome again. In this section, we're going to dive into Amazon SageMaker and its suite of built-in machine learning algorithms, which are designed to simplify the model development and deployment. We're going to start with an overview of Amazon SageMaker, followed by hands-on labs covering its setup and key functionalities. We're also going to explore various built-in algorithms, including supervised learning models like the linear learner, XG Boost and Live GBM, as well as clustering techniques like the K means clustering and hierarchical clustering. We also cover specialized algorithms for deep learning, time series forecasting, NLP, anomaly detection, and topic modeling. We'll also discuss reinforcement learning capabilities and feature extraction techniques. We'll also focus on hyper parameter tuning in order to optimize the model performance, including a hands-on lab for running a hyper parameter tuning job.

Contents