Lesson 1
Introduction to Machine Learning
Overview of key background around Machine Learning and preparing you to be successful in the rest of this course.
Introduction to Machine Learning
Course
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.
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.
Built in collaboration with
AWS
Intermediate
4 weeks
Real-world Projects
Completion Certificate
Last Updated February 26, 2024
Feature engineering • Machine learning fluency • AI business context • Machine learning use cases
Pandas • Basic probability • Intermediate Python
Course Lessons
Lesson 2
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.
Lesson 3
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.
Lesson 4
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.
Lesson 5
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
Lesson 6 • Project
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 The Best
Matt Maybeno
Principal Software Engineer
Matt is a Principal Software Engineer at SOCi. With a masters in Bioinformatics from SDSU, he utilizes his cross domain expertise to build solutions in NLP and predictive analytics.
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