Introduction to Machine Learning

Course

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.

Built in collaboration with

AWS

Intermediate
4 weeks
Last Updated November 5, 2024

Skills you'll learn:

Feature engineeringMachine learning fluency

Prerequisites:

PandasBasic probabilityIntermediate PythonJupyter notebooksBasic Python

Intermediate

4 weeks

Last Updated November 5, 2024

Skills you'll learn:

Feature engineering • Machine learning fluency • AI business context • Machine learning use cases

Prerequisites:

Pandas • Basic probability • Intermediate Python

Course Lessons

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.

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.

The Udacity Difference

Combine technology training for employees with industry experts, mentors, and projects, for critical thinking that pushes innovation. Our proven upskilling system goes after success—relentlessly.

Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

Gain proven experience
Retain knowledge longer
Apply new skills immediately

Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.