Description
Python Essentials for MLOps is a course designed to provide learners with the fundamental Python skills needed to succeed in an MLOps role. This course covers the basics of the Python programming language, including data types, functions, modules and testing techniques. It also covers how to work effectively with data sets and other data science tasks with Pandas and NumPy. Through a series of hands-on exercises, learners will gain practical experience working with Python in the context of an MLOps workflow. By the end of the course, learners will have the necessary skills to write Python scripts for automating common MLOps tasks. This course is ideal for anyone looking to break into the field of MLOps or for experienced MLOps professionals who want to improve their Python skills.
What you will learn
Introduction to Python
This week, you will learn how to effectively use variables, logic, and Python’s data structures to load, persist, and iterate over data. You will apply these data structures to solve different problems as well as extract data from them.
Python Functions and Classes
This week, you will learn how to create functions, classes, and methods. These are the basis of almost any program you might create with Python. Functions and classes are useful for organizing code, increasing maintainability and code reuse.
Testing in Python
This week, you will learn the basics of Python testing. From a brief overview of the standard library to using a more modern approach with Pytest, one of the most popular testing libraries in Python. By the end of this week, you should be comfortable working with existing tests, creating new tests, and debugging test failures.
Introduction to Pandas and NumPy
This week, you will learn how to work with data using Pandas and NumPy. From loading and reading datasets from different sources to plotting graphs and exploring common problems in data. Pandas will allow you to perform transformations and export your data into different formats, and NumPy will boost your ability to work with numerical data.
