Small Datasets in Machine Learning

Overview

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.

Syllabus

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.

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.

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.

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.

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.