Python Loggers for Machine Learning, A.I. & Data Science
Learn the Google way of using Annotations and Loggers in Python for effective logging in Data Science applications. Ideal for entrepreneurs, data scientists, ML engineers, and finance professionals.
What you’ll learn
- Learn the Google way of using Annotations and Loggers
- Construct from scratch the logger you need
- Loggers simplified! No need to panick. They are broken down in a simple way
- Learn why, and how to use them!
The logging module in Python is a ready-to-use and powerful module that is designed to meet the needs of beginners as well as enterprise teams. It is used by most of the third-party Python libraries, so you can integrate your log messages with the ones from those libraries to produce a homogeneous log for your application.
Logging is a very useful tool in a programmer’s toolbox. It can help you develop a better understanding of the flow of a program and discover scenarios that you might not even have thought of while developing.
Logs provide developers with an extra set of eyes that are constantly looking at the flow that an application is going through. They can store information, like which user or IP accessed the application. If an error occurs, then they can provide more insights than a stack trace by telling you what the state of the program was before it arrived at the line of code where the error occurred.
Python provides a logging system as a part of its standard library, so you can quickly add logging to your application. In this article, you will learn why using this module is the best way to add logging to your application as well as how to get started quickly, and you will get an introduction to some of the advanced features available.
In this course we learn how to effectively use Loggers and Annotations in Python programs , specifically designed for Data Science applications , involving Machine Learning and Artificial Intelligence.
We follow the Google way as these are used by Google employees and Software enterpreneurs.
Who this course is for:
- Entrepreneurs
- Data Scientists & ML Engineers
- Quant modellers & researchers
- Finance professionals
- PhD Students & Masters students
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