Python Essentials for MLOps (Machine Learning Operations) 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.
Python Essentials for MLOps
This course is part of MLOps | Machine Learning Operations Specialization
Instructors: Noah Gift
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(191 reviews)
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What you'll learn
Work with logic in Python, assigning variables and using different data structures.
Write, run and debug tests using Pytest to validate your work.
Interact with APIs and SDKs to build command-line tools and HTTP APIs to solve and automate Machine Learning problems.
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There are 5 modules in this course
In this module, 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.
What's included
19 videos12 readings4 assignments2 discussion prompts4 ungraded labs
In this module, 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.
What's included
17 videos12 readings5 assignments5 ungraded labs
In this module, 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 module, you should be comfortable working with existing tests, creating new tests, and debugging test failures.
What's included
17 videos6 readings4 assignments3 ungraded labs
In this module, 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.
What's included
17 videos6 readings4 assignments3 ungraded labs
In this module, you’ll grasp the basics of how to create and use APIs with Python using HTTP and command-line tools. We’ll go through all the details you need to know to create your own command-line tools and HTTP APIs to expose Machine Learning models.
What's included
22 videos8 readings4 assignments4 ungraded labs
Instructors
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Recommended if you're interested in Machine Learning
Duke University
Duke University
Duke University
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