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⇱ Foundations of AI Engineering | Coursera

Foundations of AI Engineering

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Learn Python programming, from basic syntax to advanced functions and file handling.

  • Master data science tools like NumPy and Pandas for data manipulation and analysis.

  • Gain an understanding of linear algebra, calculus, and probability for machine learning.

  • Apply statistical analysis techniques to real-world data through hands-on projects.

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Recently updated!

February 2026

Assessments

6 assignments

Taught in English

Build your subject-matter expertise

This course is part of the AI Engineering Masterclass: From Zero to AI Hero Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This course features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain a comprehensive foundation in AI engineering, starting with the fundamentals of Python programming and advancing through key data science and machine learning concepts. The course emphasizes hands-on projects that will solidify your understanding of these essential skills, providing a deep dive into Python, data science tools, and mathematics necessary for machine learning. By mastering these core concepts, you'll be equipped to approach AI engineering challenges confidently. The course is structured to guide you through each key area, beginning with Python programming basics. You will learn how to work with Python syntax, data structures, functions, and file handling, all necessary for real-world applications. As you progress, you'll explore data science essentials using NumPy and Pandas, working on projects that teach you data manipulation, visualization, and analysis. The course culminates with a deeper dive into the mathematics required for machine learning, including linear algebra, calculus, and probability. This course is perfect for aspiring AI engineers, data scientists, and those interested in pursuing machine learning. No prior experience is required, though a basic understanding of programming and mathematics will be helpful. The course is designed for beginners but includes complex mathematical concepts for those ready to delve deeper. By the end of the course, you will be able to write Python code for AI-related applications, clean and manipulate data using Pandas, visualize data with Matplotlib, apply machine learning math concepts, and execute probability and statistics techniques in data analysis and model-building projects.

In this module, we will introduce you to the fundamental concepts of Python programming, including development setup and basic syntax. You will explore control flow, functions, and data structures while applying your knowledge in hands-on projects. By the end, you'll be ready to write efficient, Pythonic code.

What's included

8 videos2 readings1 assignment

8 videos•Total 194 minutes
  • Introduction to Week 1 Python Programming Basics•1 minute
  • Day 1: Introduction to Python and Development Setup•21 minutes
  • Day 2: Control Flow in Python•33 minutes
  • Day 3: Functions and Modules•23 minutes
  • Day 4: Data Structures (Lists, Tuples, Dictionaries, Sets)•31 minutes
  • Day 5: Working with Strings•24 minutes
  • Day 6: File Handling•23 minutes
  • Day 7: Pythonic Code and Project Work•39 minutes
2 readings•Total 20 minutes
  • Introduction to the Course 'Foundations of AI Engineering'•10 minutes
  • Full Specialization Resources•10 minutes
1 assignment•Total 15 minutes
  • Week 1: Python Programming Basics - Assessment•15 minutes

In this module, we will cover the essential tools for data science, from NumPy for numerical operations to Pandas for data manipulation. You'll also gain skills in data visualization and work on an EDA project, applying your knowledge to extract insights from real-world datasets.

What's included

8 videos1 assignment

8 videos•Total 155 minutes
  • Introduction to Week 2 Data Science Essentials•1 minute
  • Day 1: Introduction to NumPy for Numerical Computing•23 minutes
  • Day 2: Advanced NumPy Operations•22 minutes
  • Day 3: Introduction to Pandas for Data Manipulation•20 minutes
  • Day 4: Data Cleaning and Preparation with Pandas•24 minutes
  • Day 5: Data Aggregation and Grouping in Pandas•15 minutes
  • Day 6: Data Visualization with Matplotlib and Seaborn•27 minutes
  • Day 7: Exploratory Data Analysis (EDA) Project•23 minutes
1 assignment•Total 15 minutes
  • Week 2: Data Science Essentials - Assessment•15 minutes

In this module, we will dive into the mathematics behind machine learning, starting with linear algebra and advancing to calculus concepts. You’ll understand the mathematical foundation needed for building and optimizing machine learning models, while applying this knowledge to create your own linear regression model.

What's included

8 videos1 assignment

8 videos•Total 136 minutes
  • Introduction to Week 3 Mathematics for Machine Learning•1 minute
  • Day 1: Linear Algebra Fundamentals•21 minutes
  • Day 2: Advanced Linear Algebra Concepts•20 minutes
  • Day 3: Calculus for Machine Learning (Derivatives)•18 minutes
  • Day 4: Calculus for Machine Learning (Integrals and Optimization)•16 minutes
  • Day 5: Probability Theory and Distributions•25 minutes
  • Day 6: Statistics Fundamentals•19 minutes
  • Day 7: Math-Driven Mini Project – Linear Regression from Scratch•15 minutes
1 assignment•Total 15 minutes
  • Week 3: Mathematics for Machine Learning - Assessment•15 minutes

In this module, we will explore the critical concepts of probability and statistics used in machine learning. From probability theory to hypothesis testing, you will gain the tools needed to analyze and interpret data. The module also includes a hands-on project to apply these concepts to real-world data.

What's included

8 videos1 reading3 assignments

8 videos•Total 125 minutes
  • Introduction to Week 4 Probability and Statistics for Machine Learning•1 minute
  • Day 1: Probability Theory and Random Variables•19 minutes
  • Day 2: Probability Distributions in Machine Learning•17 minutes
  • Day 3: Statistical Inference – Estimation and Confidence Intervals•16 minutes
  • Day 4: Hypothesis Testing and P-Values•12 minutes
  • Day 5: Types of Hypothesis Tests•19 minutes
  • Day 6: Correlation and Regression Analysis•17 minutes
  • Day 7: Statistical Analysis Project – Analyzing Real-World Data•25 minutes
1 reading•Total 10 minutes
  • Conclusion to the Course 'Foundations of AI Engineering'•10 minutes
3 assignments•Total 90 minutes
  • Week 4: Probability and Statistics for Machine Learning - Assessment•15 minutes
  • Full Course Assessment•60 minutes
  • Full Course Practice Assessment•15 minutes

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Frequently asked questions

The "Foundations of AI Engineering" course is designed to provide a comprehensive introduction to key skills required for AI engineering, including Python programming, data science, machine learning mathematics, and statistical analysis. This course is highly relevant as AI continues to evolve and influence numerous industries, making it essential for anyone looking to pursue a career in AI, data science, or related fields to have a solid understanding of these foundational concepts.

This course covers four major topics: Python programming basics, data science essentials, mathematics for machine learning, and probability and statistics for machine learning. In Week 1, you'll learn Python fundamentals, while Week 2 focuses on tools like NumPy and Pandas for data manipulation. Week 3 delves into mathematics, including linear algebra and calculus, that are crucial for machine learning. Finally, Week 4 introduces probability, statistics, and how they are applied in AI models, culminating in a hands-on project.

After completing this course, you'll be able to write efficient Python code, clean and analyze data using tools like Pandas and Matplotlib, and understand the mathematical concepts behind machine learning models. You'll also be capable of applying statistical techniques to make data-driven decisions and build basic machine learning models. The course prepares you to move forward with more advanced AI and machine learning studies.

This course is beginner-friendly, and no prior knowledge of programming or data science is required. However, having a basic understanding of mathematics, such as algebra, would be helpful. The course is structured to guide you through the essentials, so even if you're new to Python or AI, you can follow along and build your skills progressively.

This course is for anyone interested in pursuing a career in AI engineering, data science, or machine learning. It is perfect for beginners who want to build a strong foundation in these fields, as well as for professionals looking to enhance their skills in Python programming and data science essentials.

The course consists of approximately 12 hours of video content. While the total duration can vary depending on your pace and how much time you dedicate to the hands-on projects and exercises, it can typically be completed in a few weeks, assuming an average of a few hours per week.

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when 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.

Financial aid available,