In this course, you'll start learning what machine learning is by being introduced to the high level concepts through AWS SageMaker. You'll begin by using SageMaker Studio to perform exploratory data analysis. Know how and when to apply the basic concepts of machine learning to real world scenarios. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you'll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon.
Overview
Syllabus
- Introduction to Machine Learning
- Overview of key background around Machine Learning and preparing you to be successful in the rest of this course.
- Exploratory Data Analysis
- Use AWS SageMaker Studio to access S3 datasets and perform data analysis, feature engineering with Data Wrangler and Pandas. And finally label new data using SageMaker Ground Truth.
- Machine Learning Concepts
- In this lesson you'll learn about ML Lifecycles, how to differentiate between supervised vs. unsupervised ML, regression methods, and classification methods.
- Model Deployment Workflow
- In this lesson you'll load a dataset, clean/create features, train a regression/classification model with scikit learn, evaluate a model and tune a model's hyperparameter.
- Algorithms and Tools
- In this lesson you'll train, test, and optimize on liner, tree-based, XGBoost, and AutoGluon Tabular models. And you will also create a model using SageMaker Jumpstart
- Predict Bike Sharing Demand with AutoGluon
- Train a model using AutoGluon to predict bike sharing demand, and see how highly you can place in the competition!
Taught by
Matt Maybeno
