Introduction to Machine Learning

Overview

Gain foundational machine learning expertise using AWS SageMaker, from data preparation and exploratory analysis to deploying powerful models like XGBoost and AutoGluon. Covering the entire ML workflow, including feature engineering, model tuning, and evaluation, you'll gain practical experience with real-world data projects. Designed for those familiar with Python and data science basics, the curriculum equips you to handle diverse ML tasks confidently and effectively, making it ideal for anyone looking to apply machine learning in various professional and industry settings.

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