This course is part of the MLOps | Machine Learning Operations Specialization

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MLOps | Machine Learning Operations SpecializationDuke University

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 4 in the

MLOps | Machine Learning Operations Specialization

Intermediate Level

Some experience in working with Python, Git for version control, Docker for containerization and Kubernetes for deployment and scaling.

Approx. 22 hours to complete

English

What you will 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.

Skills you will gain

Information Engineering
MLOps
Machine Learning
Python Programming
Test Automation

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 4 in the

MLOps | Machine Learning Operations Specialization

Intermediate Level

Some experience in working with Python, Git for version control, Docker for containerization and Kubernetes for deployment and scaling.

Approx. 22 hours to complete

English

Instructors

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

44,599 Learners

12 Courses

Alfredo Deza

Adjunct Assistant Professor in the Pratt School of Engineering

22,545 Learners

8 Courses

Offered by

Duke University

Syllabus - What you will learn from this course

Week1

Week 1

5 hours to complete

Introduction to Python

5 hours to complete

19 videos (Total 63 min), 3 readings, 1 quiz

19 videos

Meet your Course Instructor: Alfredo Deza1mLesson Introduction: Variables and Types22sVariables and Assignments5mWorking with Different Data Types7mConditionals and Evaluations7mCatching and Handling Exceptions6mLesson Recap: Variables and Types27sLesson Introduction: Python Data Structures33sIntroduction to Lists3mCreating and Iterating Over Lists3mIntroduction to Dictionaries3mCreating and Iterating Over Dictionaries4mOther Data Structures: Tuples and Sets4mLesson Recap: Python Data Structures35sLesson Introduction: Adding and Extracting Data32sAdding Data to Lists3mExtracting Data from Lists4mExtracting Data from Dictionaries4mLesson Recap: Adding and Extracting Data34s

3 readings

Meet your Supporting Instructor: Noah Gift10mCourse Structure and Discussion Etiquette10mMinimal Python book: Storing Data10m

1 practice exercise

Python Basics30m

Week2

Week 2

5 hours to complete

Python Functions and Classes

5 hours to complete

17 videos (Total 60 min), 2 readings, 1 quiz

17 videos

Lesson Introduction: Working with Functions37sFunction Structure and Values3mFunction Arguments5mVariable and Keyword Arguments5mLesson Recap: Working with Functions31sLesson Introduction: Building Classes and Methods36sIntroduction to Classes7mUsing a Constructor6mAdding Methods4mClass Inheritance5mLesson Recap: Building Classes and Methods37sLesson Introduction: Modules and Advanced Usages38sIntroduction to Python Modules3mWorking with Imports4mWorking with Python Scripts4mVirtual Environments and Dependencies5mLesson Recap: Modules and Advanced Usages37s

2 readings

Minimal Python book: Create functions10mPython for Beginners Learning Path10m

1 practice exercise

Python Functions and Classes30m

Week3

Week 3

5 hours to complete

Testing in Python

5 hours to complete

17 videos (Total 66 min)

17 videos

Lesson Introduction: Writing and Executing Tests33sMotivations for Testing in Python5mTesting Conventions8mTesting with pytest6mLesson Recap: Writing and Executing Tests43sLesson Introduction: Writing Useful Tests39sUsing Plan Asserts in pytest5mWriting Test Classes4mTest Classes vs. Test Functions3mParameterizing Tests7mLesson Recap: Writing Useful Tests47sLesson Introduction: Testing Failures47sTest Failure Output5mPython Debugging with PDB5mOther pytest Runner Options3mpytest Fixtures6mLesson Recap: Testing Failures41s

1 practice exercise

Python Testing30m

Week4

Week 4

5 hours to complete

Introduction to Pandas and NumPy

5 hours to complete

17 videos (Total 60 min)

17 videos

Lesson Introduction: Basic Pandas Usage31sIntroduction to Pandas5mLoading Data into Pandas5mWriting Data from Pandas DataFrames3mExploratory Analysis with Pandas6mLesson Recap: Basic Pandas Usage46sLesson Introduction: Working with DataFrames24sCommon DataFrame Operations7mManipulating Text in DataFrames4mApplying Functions with Pandas3mVisualizing Data with Pandas4mLesson Recap: Working with DataFrames37sLesson Introduction: NumPy Basics24sIntroduction to NumPy Arrays5mCommon NumPy Array Operations3mMore NumPy Array Operations6mLesson Recap: NumPy Basics28s

1 practice exercise

Pandas and NumPy30m

About the MLOps | Machine Learning Operations Specialization

This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You'll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps

Through this series, you will begin to learn skills for various career paths: 1. Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making. 2. Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems. 3. Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner. 4. Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Python Essentials for MLOps

About this Course

What you will learn

Skills you will gain