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⇱ AI Fundamentals for Non-Data Scientists | Coursera


AI Fundamentals for Non-Data Scientists

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AI Fundamentals for Non-Data Scientists

This course is part of AI For Business Specialization

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Gain insight into a topic and learn the fundamentals.
4.8

1,046 reviews

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
95%
Most learners liked this course

Gain insight into a topic and learn the fundamentals.
4.8

1,046 reviews

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
95%
Most learners liked this course

Build your subject-matter expertise

This course is part of the AI For Business 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 5 modules in this course

In this course, you will go in-depth to discover how Machine Learning is used to handle and interpret Big Data. You will get a detailed look at the various ways and methods to create algorithms to incorporate into your business with such tools as Teachable Machine and TensorFlow. You will also learn different ML methods, Deep Learning, as well as the limitations but also how to drive accuracy and use the best training data for your algorithms. You will then explore GANs and VAEs, using your newfound knowledge to engage with AutoML to help you start building algorithms that work to suit your needs. You will also see exclusive interviews with industry leaders, who manage Big Data for companies such as McDonald's and Visa. By the end of this course, you will have learned different ways to code, including how to use no-code tools, understand Deep Learning, how to measure and review errors in your algorithms, and how to use Big Data to not only maintain customer privacy but also how to use this data to develop different strategies that will drive your business.

In this module, you will be introduced to Big Data and examine how machine learning is used throughout various business segments. You will also learn how data is analyzed and extracted, and how digital technologies have been used to expand and transform businesses. You will also get a detailed look at data management tools and how they are best implemented and the value of data warehouses. By the end of this module, you will have gained insight into how machine learning can be used as a general-purpose technology, and some best techniques and practices for data mining.

What's included

11 videos1 reading2 assignments

11 videosβ€’Total 93 minutes
  • AI for Business Introductionβ€’8 minutes
  • Course Introductionβ€’2 minutes
  • Big Data Overviewβ€’9 minutes
  • Big Data Analysisβ€’6 minutes
  • Data Management Toolsβ€’7 minutes
  • Data Management Infrastructureβ€’10 minutes
  • Data Analysis: Extracting Intelligence from Big Dataβ€’11 minutes
  • Introduction to Artificial Intelligenceβ€’9 minutes
  • Machine Learning Overviewβ€’16 minutes
  • Reinforcement Learningβ€’8 minutes
  • A Detailed View of Machine Learningβ€’8 minutes
1 readingβ€’Total 30 minutes
  • Module 1 Slidesβ€’30 minutes
2 assignmentsβ€’Total 60 minutes
  • Module 1 Quizβ€’30 minutes
  • Practice Quiz #1β€’30 minutes

In this module, you will get an in-depth look at contrasting Machine Learning methods, including logistic regression and neural nets. You will also learn about Deep Learning and its relationship to neural networks and how to best optimize Machine Learning algorithms. Lastly, you will be introduced to loss functions and how to best measure and review errors to maintain the integrity of your algorithms. By the end of this module, you will have learned about Machine Learning methods, the limitations and value of Deep Learning, how best to drive precision and accuracy in algorithms, and how to get the best training data for those algorithms.

What's included

13 videos1 reading2 assignments

13 videosβ€’Total 74 minutes
  • Specific Machine Learning Methods: A Deep Diveβ€’19 minutes
  • Intro to Model Selectionβ€’4 minutes
  • Feature Engineering and Deep Learning Introductionβ€’4 minutes
  • Deep Learningβ€’7 minutes
  • How Deep Learning Worksβ€’8 minutes
  • Limitations of Deep Learningβ€’3 minutes
  • Evaluating ML Performanceβ€’3 minutes
  • Common Loss Functionsβ€’6 minutes
  • Tradeoffs Between Loss Functionsβ€’3 minutes
  • How is Training Data Acquired?β€’5 minutes
  • The Over-Fitting Problemβ€’5 minutes
  • Test Dataβ€’3 minutes
  • Examples of End-to-End Work Flowβ€’5 minutes
1 readingβ€’Total 30 minutes
  • Module 2 Slidesβ€’30 minutes
2 assignmentsβ€’Total 60 minutes
  • Module 2 Quizβ€’30 minutes
  • Practice Quiz #2β€’30 minutes

In this module, you will take a look at Machine Learning within natural language processing and using generative modeling to create new data. You will also focus on AutoML and how to best utilize automated processes to make your algorithms more efficient. You will also review the no-code Machine Learning tool Teachable Machine, which serves to make Deep and Machine Learning more accessible. By the end of this module, you will be able to use AutoML in your algorithms and be able to navigate and use Teachable Machine in practice for no-code solutions to building an algorithm.

What's included

8 videos1 reading2 assignments

8 videosβ€’Total 45 minutes
  • Natural Language Processingβ€’8 minutes
  • GANs and VAEsβ€’7 minutes
  • Intro to AutoMLβ€’2 minutes
  • Using AutoMLβ€’4 minutes
  • Teachable Machineβ€’6 minutes
  • TensorFlow Playgroundβ€’4 minutes
  • ML Operationsβ€’4 minutes
  • Chicken and Eggβ€’12 minutes
1 readingβ€’Total 10 minutes
  • Module 3 Slidesβ€’10 minutes
2 assignmentsβ€’Total 60 minutes
  • Module 3 Quizβ€’30 minutes
  • Practice Quiz #3β€’30 minutes

In this module, you will hear from an industry leader and gain valuable insight into data sampling and building realistic usable models. Ed Lee, VP of Global Menu Strategy & Global Marketing at McDonald's, will allow you to review real-world solutions and how they handle data issues as one of the most successful global brands. By the end of this module, you will have heard from a top industry expert in their field and gained firsthand knowledge and understanding of how Big Data plays into maintaining privacy in data and also utilizing that data to enhance your marketing, content, and refine your algorithms.

What's included

1 video1 assignment1 peer review

1 videoβ€’Total 13 minutes
  • Interview With Ed Leeβ€’13 minutes
1 assignmentβ€’Total 30 minutes
  • Practice Quiz #4β€’30 minutes
1 peer reviewβ€’Total 60 minutes
  • Module 4 β€’60 minutes

In this module, you will explore multiple aspects of generative AI. Not only will you gain an understanding of how it makes predictions and generates content, but you will also gain an understanding of how large language models work. Diving deeper, you will explore the generative AI stack as well as foundation models and their versatility in performing a broad range of tasks. Reflecting on research studies, you will examine the implications of generative AI on work and productivity, including the potential for both human displacement and enhancement. You will gain insights for crafting instructions to improve the quality of output from large language modules and explore how a company building an application on top of foundation models may gain a competitive advantage.

What's included

8 videos1 reading2 assignments

8 videosβ€’Total 61 minutes
  • Generative AI Overviewβ€’11 minutes
  • Implications of Generative AI on Workβ€’10 minutes
  • Generative AI's Implication on Productivityβ€’6 minutes
  • The Generative AI Stackβ€’4 minutes
  • Foundation Modelsβ€’9 minutes
  • Prompt Engineering Principles Improving Output Qualityβ€’9 minutes
  • Customizing LLM Outputβ€’7 minutes
  • Differentiation Gaining Competitive Advantageβ€’6 minutes
1 readingβ€’Total 10 minutes
  • Module 5 Slidesβ€’10 minutes
2 assignmentsβ€’Total 90 minutes
  • Module 5 Quizβ€’60 minutes
  • Practice Quiz #5β€’30 minutes

Earn a career certificate

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Instructors

Instructor ratings
4.8 (331 ratings)
University of Pennsylvania
8 Coursesβ€’297,677 learners
University of Pennsylvania
3 Coursesβ€’97,375 learners

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Showing 3 of 1046

CA
Β·

Reviewed on Mar 18, 2025

A very well put together and presented course that laid a good foundation. Not too technical but enough to understand some of the technical implications.

RP
Β·

Reviewed on Jan 17, 2024

This was an excellent introductory course that explained the concepts in clear and understandable fashion. A solid foundation to build upon.

JH
Β·

Reviewed on Aug 11, 2025

This a very well structure course for non Data Scientist professionals. Easy to follow and understand. Each module was very well presented and explained by the trainer.

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.

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.

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