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Alfredo Deza
Master Python for efficient Machine Learning Operations by building strong programming foundations, creating MLOps automation, and gaining applicable experience.
Read more
Master Python for efficient Machine Learning Operations by building strong programming foundations, creating MLOps automation, and gaining applicable experience.
Read more
Master Python for efficient Machine Learning Operations by building strong programming foundations, creating MLOps automation, and gaining applicable experience.
- Fundamentals of Python programming: Data types, functions, modules
- Testing techniques
- Data manipulation and analysis
- Work with datasets using Pandas
- Leveraging NumPy for data science
- Hands-on coding exercises
- Apply Python in MLOps workflows
This comprehensive course covers the essential Python skills for succeeding in MLOps roles. Through hands-on exercises, you'll learn:
- Core Python programming concepts
- Data manipulation and analysis
- Containerization of ML models
- GitHub Actions for automation
Whether you're new to MLOps or an experienced professional, this course equips you with the foundational Python skills to excel in machine learning operations roles.
Register for this course and see more details by visiting:
OpenCourser.com/course/zz9oyy/python
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What's inside
Learning objectives
- Interact with apis and sdks to build command-line tools and http apis to solve and automate machine learning problems.
- 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.
- Work with logic in python, assigning variables and using different data structures.
- Write, run and debug tests using pytest to validate your work.
Syllabus
Introduction to Python
• Module 1 (9 hours to complete)
◦ Meet your Course Instructor: Alfredo Deza (video, 1 minute)
◦ Lesson Introduction: Variables and Types (video, 0 minutes)
Read more
Syllabus
Introduction to Python
• Module 1 (9 hours to complete)
◦ Meet your Course Instructor: Alfredo Deza (video, 1 minute)
◦ Lesson Introduction: Variables and Types (video, 0 minutes)
Read more
◦ Variables and Assignments (video, 5 minutes)
◦ Working with Different Data Types (video, 7 minutes)
◦ Conditionals and Evaluations (video, 7 minutes)
◦ Catching and Handling Exceptions (video, 6 minutes)
◦ Lesson Recap: Variables and Types (video, 0 minutes)
◦ Lesson Introduction: Python Data Structures (video, 0 minutes)
◦ Introduction to Lists (video, 3 minutes)
◦ Creating and Iterating Over Lists (video, 3 minutes)
◦ Introduction to Dictionaries (video, 3 minutes)
◦ Creating and Iterating Over Dictionaries (video, 4 minutes)
◦ Other Data Structures: Tuples and Sets (video, 4 minutes)
◦ Lesson Recap: Python Data Structures (video, 0 minutes)
◦ Lesson Introduction: Adding and Extracting Data (video, 0 minutes)
◦ Adding Data to Lists (video, 3 minutes)
◦ Extracting Data from Lists (video, 4 minutes)
◦ Extracting Data from Dictionaries (video, 4 minutes)
◦ Lesson Recap: Adding and Extracting Data (video, 0 minutes)
◦ Connect with your instructor (reading, 10 minutes)
◦ Meet your Supporting Instructor: Noah Gift (reading, 10 minutes)
◦ Course Structure and Discussion Etiquette (reading, 10 minutes)
◦ Getting Started and Course Best Practices (reading, 10 minutes)
◦ Key Terms (reading, 10 minutes)
◦ Lesson Reflection (reading, 10 minutes)
◦ Minimal Python book: Storing Data (reading, 10 minutes)
◦ Quiz-Variables and Types (quiz, 30 minutes)
◦ Quiz-Introduction to Python Data Structures (quiz, 30 minutes)
◦ Quiz-Adding and Extracting Data from Data Structures (quiz, 30 minutes)
◦ Week 1-Final Graded Quiz-Python Basics (quiz, 30 minutes)
◦ Meet and Greet (optional) (discussion prompt, 10 minutes)
◦ Let Us Know if Something's Not Working (discussion prompt, 10 minutes)
◦ Variables and Types (ungraded lab, 60 minutes)
◦ Data Structures (ungraded lab, 60 minutes)
◦ Adding and Extracting Data (ungraded lab, 60 minutes)
◦ Sandbox Week One (ungraded lab, 60 minutes)
****
Python Functions and Classes
• Module 2 (10 hours to complete)
◦ Lesson Introduction: Working with Functions (video, 0 minutes)
◦ Function Structure and Values (video, 3 minutes)
◦ Function Arguments (video, 5 minutes)
◦ Variable and Keyword Arguments (video, 5 minutes)
◦ Lesson Recap: Working with Functions (video, 0 minutes)
◦ Lesson Introduction: Building Classes and Methods (video, 0 minutes)
◦ Introduction to Classes (video, 7 minutes)
◦ Using a Constructor (video, 6 minutes)
◦ Adding Methods (video, 4 minutes)
◦ Class Inheritance (video, 5 minutes)
◦ Lesson Recap: Building Classes and Methods (video, 0 minutes)
◦ Lesson Introduction: Modules and Advanced Usages (video, 0 minutes)
◦ Introduction to Python Modules (video, 3 minutes)
◦ Working with Imports (video, 4 minutes)
◦ Working with Python Scripts (video, 4 minutes)
◦ Virtual Environments and Dependencies (video, 5 minutes)
◦ Lesson Recap: Modules and Advanced Usages (video, 0 minutes)
◦ Minimal Python book: Create functions (reading, 10 minutes)
◦ Generators (reading, 10 minutes)
◦ Inheritance (reading, 10 minutes)
◦ Ungraded Lab Sandbox Key Terms (reading, 10 minutes)
◦ Python for Beginners Learning Path (reading, 10 minutes)
◦ Understanding 3rd Party Packaging (reading, 10 minutes)
◦ Quiz-Functions (quiz, 30 minutes)
◦ Quiz-Ungraded Lab Python Classes Sandbox (quiz, 30 minutes)
◦ Quiz-Building Classes and Methods (quiz, 30 minutes)
◦ Quiz-Modules (quiz, 30 minutes)
◦ Python Functions and Classes (quiz, 30 minutes)
◦ Functions (ungraded lab, 60 minutes)
◦ Python Functions Sandbox (ungraded lab, 60 minutes)
◦ Python Classes (ungraded lab, 60 minutes)
◦ Python Classes Sandbox (ungraded lab, 60 minutes)
◦ Python Modules (ungraded lab, 60 minutes)
Testing in Python
• Module 3 (7 hours to complete)
◦ Lesson Introduction: Writing and Executing Tests (video, 0 minutes)
◦ Motivations for Testing in Python (video, 5 minutes)
◦ Testing Conventions (video, 8 minutes)
◦ Testing with pytest (video, 6 minutes)
◦ Lesson Recap: Writing and Executing Tests (video, 0 minutes)
◦ Lesson Introduction: Writing Useful Tests (video, 0 minutes)
◦ Using Plan Asserts in pytest (video, 5 minutes)
◦ Writing Test Classes (video, 4 minutes)
◦ Test Classes vs. Test Functions (video, 3 minutes)
◦ Parameterizing Tests (video, 7 minutes)
◦ Lesson Recap: Writing Useful Tests (video, 0 minutes)
◦ Lesson Introduction: Testing Failures (video, 0 minutes)
◦ Test Failure Output (video, 5 minutes)
◦ Python Debugging with PDB (video, 5 minutes)
◦ Other pytest Runner Options (video, 3 minutes)
◦ pytest Fixtures (video, 6 minutes)
◦ Lesson Recap: Testing Failures (video, 0 minutes)
◦ Quiz-Introduction to Testing (quiz, 30 minutes)
◦ Quiz: Writing Useful Tests (quiz, 30 minutes)
◦ Quiz- Testing Failures (quiz, 30 minutes)
◦ Python Testing (quiz, 30 minutes)
◦ Testing Conventions (ungraded lab, 60 minutes)
◦ Testing with Pytest (ungraded lab, 60 minutes)
◦ Test Failures (ungraded lab, 60 minutes)
Introduction to Pandas and NumPy
• Module 4 (7 hours to complete)
◦ Lesson Introduction: Basic Pandas Usage (video, 0 minutes)
◦ Introduction to Pandas (video, 5 minutes)
◦ Loading Data into Pandas (video, 5 minutes)
◦ Writing Data from Pandas DataFrames (video, 3 minutes)
◦ Exploratory Analysis with Pandas (video, 6 minutes)
◦ Lesson Recap: Basic Pandas Usage (video, 0 minutes)
◦ Lesson Introduction: Working with DataFrames (video, 0 minutes)
◦ Common DataFrame Operations (video, 7 minutes)
◦ Manipulating Text in DataFrames (video, 4 minutes)
◦ Applying Functions with Pandas (video, 3 minutes)
◦ Visualizing Data with Pandas (video, 4 minutes)
◦ Lesson Recap: Working with DataFrames (video, 0 minutes)
◦ Lesson Introduction: NumPy Basics (video, 0 minutes)
◦ Introduction to NumPy Arrays (video, 5 minutes)
◦ Common NumPy Array Operations (video, 3 minutes)
◦ More NumPy Array Operations (video, 6 minutes)
◦ Lesson Recap: NumPy Basics (video, 0 minutes)
◦ Quiz - Basic Pandas Usage (quiz, 30 minutes)
◦ Quiz-Working with DataFrames (quiz, 30 minutes)
◦ Quiz-NumPy Basics (quiz, 30 minutes)
◦ Pandas and NumPy (quiz, 30 minutes)
◦ Introduction to Pandas (ungraded lab, 60 minutes)
◦ Pandas DataFrames (ungraded lab, 60 minutes)
◦ NumPy (ungraded lab, 60 minutes)
Applied Python for MLOps
• Module 5 (9 hours to complete)
◦ Lesson Introduction: APIs and SDKs (video, 0 minutes)
◦ Installing Azure Command-Line Interface (CLI) (video, 6 minutes)
◦ AzureML Studio with Python (video, 6 minutes)
◦ Hugging Face Transformers (video, 6 minutes)
◦ Hugging Face Datasets (video, 8 minutes)
◦ Azure Open Datasets (video, 6 minutes)
◦ Lesson Recap: APIs and SDKs (video, 0 minutes)
◦ Lesson Introduction: Automation with Command-Line Tools (video, 0 minutes)
◦ Creating a Single File Script (video, 4 minutes)
◦ Using the ArgParse Framework (video, 6 minutes)
◦ Declaring Dependencies (video, 3 minutes)
◦ Using the Click Framework (video, 7 minutes)
◦ Packaging your Project (video, 4 minutes)
◦ Solving a Machine Learning Problem with a CLI Tool (video, 8 minutes)
◦ Lesson Recap: Automation with Command-Line Tools (video, 0 minutes)
◦ Lesson Introduction: Building Machine Learning APIs (video, 0 minutes)
◦ Introduction to Flask Framework (video, 6 minutes)
◦ Building an API with Flask (video, 8 minutes)
◦ Introduction to the FastAPI Framework (video, 7 minutes)
◦ Building an API with FastAPI (video, 6 minutes)
◦ Python API Best Practices (video, 7 minutes)
◦ Lesson Recap: Building Machine Learning APIs (video, 0 minutes)
◦ External Lab: GPU Powered MLOps Template (reading, 10 minutes)
◦ Next Steps (reading, 10 minutes)
◦ Quiz-Working with APIs and SDKs (quiz, 30 minutes)
◦ Quiz-Automating with the CLI (quiz, 30 minutes)
◦ Quiz-Working with GitHub GPU MLOps Template (quiz, 30 minutes)
◦ Automation with Python (quiz, 30 minutes)
◦ MLOps CLI (ungraded lab, 60 minutes)
◦ Linux Desktop Sandbox (ungraded lab, 60 minutes)
◦ Jupyter Final Sandbox (ungraded lab, 60 minutes)
◦ VSCode Final Sandbox (ungraded lab, 60 minutes)
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops essential and foundational Python programming skills, including variables, functions, modules, and testing techniques
Enhances data manipulation and analysis capabilities using Pandas and NumPy libraries
Suitable for beginners seeking a strong foundation in Python for MLOps roles
Provides hands-on coding exercises to reinforce learning and enhance practical skills
Offers insights into MLOps automation through GitHub Actions and containerization of ML models
Taught by experienced instructor Alfredo Deza, who brings industry knowledge and expertise
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activities:
Review basic data structures
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Refresh your understanding of fundamental data structures to enhance your Python programming skills
Browse courses on
Data Structures
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Review online resources or tutorials on basic data structures
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Work through practice exercises or coding challenges related to data structures
Join a Python study group
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Engage with peers to discuss Python concepts, share knowledge, and improve your understanding
Browse courses on
Python
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Find a Python study group or create one with classmates
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Regularly attend study group meetings
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Actively participate in discussions, asking questions and sharing insights
Work through Python exercises
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Reinforce your understanding of the Python basics covered in the course
Browse courses on
Python
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Solve coding challenges in the course
-
Practice writing Python code snippets on your own
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Review and complete example Python exercises provided
Four other activities
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Complete NumPy tutorials
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Enhance your proficiency in using NumPy for numerical operations
Browse courses on
NumPy
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Find and follow online tutorials on NumPy basics
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Complete interactive exercises and examples provided in the tutorials
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Apply NumPy functions to solve practical problems
Explore the Pandas library with tutorials
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Develop proficiency in using Pandas for efficient data manipulation and analysis
Browse courses on
Pandas
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-
Identify and follow online tutorials on Pandas basics and data manipulation techniques
-
Complete hands-on exercises and examples provided in the tutorials
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Apply Pandas functions to solve practical data manipulation tasks
Develop a Python script for data analysis
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Gain hands-on experience in applying Python for real-world data analysis tasks
Browse courses on
Python Scripting
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Define a problem or dataset for analysis
-
Write a Python script to load, clean, and analyze the data
-
Generate visualizations or reports to present the results
Write a blog post on Python function optimization
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Enhance your understanding of Python functions by explaining their optimization strategies
Show steps
-
Research and gather information on Python function optimization techniques
-
Write a detailed blog post outlining the techniques and their benefits
-
Include code examples and practical tips to demonstrate the optimization strategies
Review basic data structures
Show steps
Refresh your understanding of fundamental data structures to enhance your Python programming skills
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Review online resources or tutorials on basic data structures
- Work through practice exercises or coding challenges related to data structures
Join a Python study group
Show steps
Engage with peers to discuss Python concepts, share knowledge, and improve your understanding
Browse courses on
Python
Show steps
Show steps
Show steps
- Find a Python study group or create one with classmates
- Regularly attend study group meetings
- Actively participate in discussions, asking questions and sharing insights
Work through Python exercises
Show steps
Reinforce your understanding of the Python basics covered in the course
Browse courses on
Python
Show steps
Show steps
Show steps
- Solve coding challenges in the course
- Practice writing Python code snippets on your own
- Review and complete example Python exercises provided
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete NumPy tutorials
Show steps
Enhance your proficiency in using NumPy for numerical operations
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Find and follow online tutorials on NumPy basics
- Complete interactive exercises and examples provided in the tutorials
- Apply NumPy functions to solve practical problems
Explore the Pandas library with tutorials
Show steps
Develop proficiency in using Pandas for efficient data manipulation and analysis
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Identify and follow online tutorials on Pandas basics and data manipulation techniques
- Complete hands-on exercises and examples provided in the tutorials
- Apply Pandas functions to solve practical data manipulation tasks
Develop a Python script for data analysis
Show steps
Gain hands-on experience in applying Python for real-world data analysis tasks
Browse courses on
Python Scripting
Show steps
Show steps
Show steps
- Define a problem or dataset for analysis
- Write a Python script to load, clean, and analyze the data
- Generate visualizations or reports to present the results
Write a blog post on Python function optimization
Show steps
Enhance your understanding of Python functions by explaining their optimization strategies
Show steps
Show steps
Show steps
- Research and gather information on Python function optimization techniques
- Write a detailed blog post outlining the techniques and their benefits
- Include code examples and practical tips to demonstrate the optimization strategies
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OpenCourser.com/course/zz9oyy/python
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Effort
3 to 6 hours per week for 5 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructor
Alfredo Deza
Session
March 19, 2024
- March 20, 2025
Language
English
Transcript
English
Good to know
Develops essential and foundational Python programming skills, including variables, functions, modules, and testing techniques
Enhances data manipulation and analysis capabilities using Pandas and NumPy libraries
Suitable for beginners seeking a strong foundation in Python for MLOps roles
Provides hands-on coding exercises to reinforce learning and enhance practical skills
Offers insights into MLOps automation through GitHub Actions and containerization of ML models
Taught by experienced instructor Alfredo Deza, who brings industry knowledge and expertise
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