How To Structure Machine Learning ProjectsAnd Make Machine Learning Algorithms WorkAdmond LeeBlockedUnblockFollowFollowingDec 1This article is not to show you what machine learning algorithms to learn and explain the nitty-gritty of the models to you.If you’re looking for these materials, I strongly recommend you to check out my previous article to know how to choose online courses, what online courses to choose and what books to read for deeper understanding.In fact, this article is to show you how you can really make machine learning algorithms work for your projects and how to structure them that you’d otherwise spend unnecessarily long time to optimize your models in the wrong direction.Machine Learning Yearning by Andrew NgRegardless of whether you’re a beginner or an expert in data science, chances are (and I mean 99%) you have heard of his name.(Source)His most famous course on Coursera — Machine Learning is a treasure to many students around the world..Learning how to set direction for your team to make strategic decisions at the first place is so important and this often requires years of experience.Therefore, this book is meant to make machine learning algorithms work for your projects and company by prioritizing the most promising directions, diagnosing errors in a complex machine learning system, improving your team’s productivity and so much more.A sneak preview of this book1..Setting up development and test setsTraining set — Which you run your learning algorithm on.Dev (development) set — Which you use to tune parameters, select features, and make other decisions regarding the learning algorithm..Learning curvesPlotting training error and learning curvesInterpreting learning curves: High biasInterpreting learning curves: Other casesThis chapter explains why learning curves are so important in understanding the performance of models and how to use learning curves to make decisions based on the desired level of performance.5..I hope that by showing my takeaways from this book will give you a brief overview of the book and how you can benefit from it.Ultimately, the practicality of the book will teach you how to structure your machine learning projects and make your models work for you, your team and the company.If you enjoyed this article, feel free to hit that clap button ????. More details
- What are Autoencoders? Learn How to Enhance a Blurred Image using an Autoencoder!
- Introducing Databricks Ingest: Easy and Efficient Data Ingestion from Different Sources into Delta Lake
- New Data Ingestion Network for Databricks: The Partner Ecosystem for Applications, Database, and Big Data Integrations into Delta Lake
- A gentle introduction to Hamming codes