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Small Datasets in Machine Learning

Course

This learning journey is designed to provide a comprehensive understanding of machine learning models and techniques specifically tailored for small datasets. You'll learn how to effectively utilize small datasets to build powerful models while avoiding common pitfalls associated with data scarcity. We'll cover methodologies and practical applications, allowing you to develop a strong foundation in machine learning techniques that apply to small dataset problems so you can successfully apply these techniques in your projects.

This learning journey is designed to provide a comprehensive understanding of machine learning models and techniques specifically tailored for small datasets. You'll learn how to effectively utilize small datasets to build powerful models while avoiding common pitfalls associated with data scarcity. We'll cover methodologies and practical applications, allowing you to develop a strong foundation in machine learning techniques that apply to small dataset problems so you can successfully apply these techniques in your projects.

Intermediate

4 weeks

Real-world Projects

Completion Certificate

Last Updated December 5, 2023

Skills you'll learn:
Transfer learning • Variational autoencoders
Prerequisites:
Probability and statistics • Intermediate Python • Basic machine learning

Course Lessons

Lesson 1

Introduction to Small Data in Machine Learning

Learn about small data and what you'll accomplish. Check your prerequisite knowledge and overview the tools and environment you'll be using.

Lesson 2

Machine Learning Techniques for Small Data

You will learn to identify small data as opposed to big data. You will learn about some small data techniques understand the types of problems that can be solved with small datasets.

Lesson 3

Transfer Learning and Small Data Problems

You will learn the basics of transfer learning as well as how to decide when to use transfer learning. You will see a demo of how transfer learning works.

Lesson 4

Synthetic Data and Small Data Problems

You will learn the difference between synthetic data and fake data and when you should use synthetic data. You will learn the basics of how to generate synthetic data.

Lesson 5 • Project

Project: Transfer Learning and Data Generation Solutions

In this project, you'll determine when to use different small data strategies using transfer learning and synthetic data to solve small data problems.

Taught By The Best

Photo of Matt Swaffer

Matt Swaffer

General Manager, MBS

Matt has been working in software development and data science for over 20 years. Matt's career is centered on the intersection of technology, data, and human psychology. He is passionate about using data science to have a meaningful impact on our people and our planet.

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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.

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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.

  • Get help from subject matter experts

  • Learn industry best practices

  • Gain valuable insights and improve your skills