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Ben Garside
and
Mac Bowley
From self-driving cars to determining someone's age, artificial intelligence (AI) systems trained with machine learning (ML) are being used more and more. But what is AI, and what does machine learning actually involve?
In this four-week course from the Raspberry Pi Foundation, you'll learn about different types of machine learning, and use online tools to train your own AI models. You'll find out about the types of problems that machine learning can help to solve, discuss how AI is changing the world, and think about the ethics of collecting data to train a machine learning model.
Read more
From self-driving cars to determining someone's age, artificial intelligence (AI) systems trained with machine learning (ML) are being used more and more. But what is AI, and what does machine learning actually involve?
In this four-week course from the Raspberry Pi Foundation, you'll learn about different types of machine learning, and use online tools to train your own AI models. You'll find out about the types of problems that machine learning can help to solve, discuss how AI is changing the world, and think about the ethics of collecting data to train a machine learning model.
Read more
From self-driving cars to determining someone's age, artificial intelligence (AI) systems trained with machine learning (ML) are being used more and more. But what is AI, and what does machine learning actually involve?
In this four-week course from the Raspberry Pi Foundation, you'll learn about different types of machine learning, and use online tools to train your own AI models. You'll find out about the types of problems that machine learning can help to solve, discuss how AI is changing the world, and think about the ethics of collecting data to train a machine learning model.
What you'll learn
Over the following four weeks, you will:
- Demonstrate several working machine learning models
- Explain the different types of machine learning, and the problems that they are suitable for
- Compare supervised, unsupervised, and reinforcement learning
- Discuss the ethical issues surrounding machine learning and AI
Register for this course and see more details by visiting:
OpenCourser.com/course/jjylku/introduction
What's inside
Learning objectives
- Demonstrate several working machine learning models
- Explain the different types of machine learning, and the problems that they are suitable for
- Compare supervised, unsupervised, and reinforcement learning
- Discuss the ethical issues surrounding machine learning and ai
- Demonstrate several working machine learning models
- Explain the different types of machine learning, and the problems that they are suitable for
- Compare supervised, unsupervised, and reinforcement learning
- Discuss the ethical issues surrounding machine learning and ai
Syllabus
This course will cover:
The history of AI
What are AI and machine learning?
Using AI for classification
Read more
Syllabus
This course will cover:
The history of AI
What are AI and machine learning?
Using AI for classification
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Delves into the use of machine learning for real-world applications
Utilizes online tools for practical machine learning model training
Examines the ethical implications of data collection for machine learning algorithms
Suitable for learners with a basic understanding of programming concepts
Incorporates hands-on activities to enhance learning
Provides a comprehensive overview of machine learning concepts
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
Beginner-friendly ai and ml overview
According to learners, this course offers a largely positive and accessible introduction to AI and Machine Learning. Students highlight its clear, concise explanations and easy-to-grasp concepts, making it perfect for absolute beginners and non-technical individuals. The focus on ethical considerations is frequently cited as a valuable and important aspect. While the course provides helpful hands-on activities using online tools, some with prior technical knowledge felt the practical components were too basic and wished for more technical depth or coding practice.
Offers introductory activities, but not deep coding or complex projects.
"The hands-on activities using online tools were particularly helpful."
"The 'hands-on' parts are very basic, using pre-built tools rather than actual coding."
"I was hoping for a bit more practical application."
Modules are digestible, and the course flows well for foundational understanding.
"The pacing was perfect for me."
"Everything is broken down into easily digestible modules."
"It covers the syllabus well."
Reviewers highlight the valuable and important ethical considerations.
"I appreciated the focus on ethical considerations."
"The ethical discussions were a highlight, making me think critically about AI's impact."
"I really enjoyed the ethical considerations discussed; it's an important aspect often overlooked in intro courses."
"I particularly liked the ethical discussion."
Highly praised for demystifying complex AI/ML topics for newcomers.
"The lectures are clear, concise, and the explanations are easy to grasp, even for someone with no prior experience."
"As a non-technical person, I found the explanations of complex topics like supervised and unsupervised learning incredibly accessible."
"This course provided a fantastic high-level overview of AI and ML. It demystifies complex topics and makes them approachable."
"Everything is broken down into easily digestible modules. The examples helped solidify my understanding."
Some sections felt brief or slightly out of place for a few learners.
"My only minor critique is that the 'writing a good ML resource' section felt a bit out of place and rushed."
"I felt the segment on data preparation was a bit brief."
Excellent for newcomers, but lacks depth for those with programming skills.
"It's great for absolute beginners, but not enough for someone with a coding background already."
"Perhaps better suited for those completely new to tech."
"The course is quite basic, almost too basic for me who has some prior knowledge in programming."
"If you want to get your hands dirty with actual ML models, this isn't it. However, if you know nothing, it's a gentle start."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Introduction to Machine Learning and AI with these
activities:
Review Intro to AI concepts
Show steps
Review basic concepts of AI, such as machine learning, supervised learning, unsupervised learning, and reinforcement learning.
Browse courses on
Artificial Intelligence
Show steps
-
Read the course syllabus and skim the course content.
-
Watch videos or tutorials on the basics of AI.
-
Complete practice exercises on basic AI concepts.
Compile a collection of AI resources
Show steps
Organize and expand your knowledge of AI by compiling a collection of useful resources.
Show steps
-
Identify different types of AI resources, such as articles, tutorials, books, and datasets.
-
Search for and gather resources that align with your learning goals.
-
Organize the resources into a structured collection, such as a folder or a shared document.
-
Regularly review and update your collection as you progress in your learning.
Read and review 'Artificial Intelligence: A Modern Approach'
Show steps
Gain a comprehensive understanding of the foundational concepts and algorithms in AI.
Show steps
-
Read the book and take notes on the key concepts.
-
Complete the exercises and assignments in the book.
-
Write a review of the book, summarizing your key takeaways.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice writing simple AI algorithms
Show steps
Apply your understanding of AI algorithms by writing code to implement them.
Browse courses on
Supervised Learning
Show steps
-
Choose a simple AI algorithm to implement, such as linear regression or decision trees.
-
Implement the algorithm in a programming language of your choice.
-
Test your implementation on a dataset.
-
Refine your implementation based on the results of your testing.
Follow tutorials on specific AI algorithms or techniques
Show steps
Expand your knowledge of AI by following tutorials on specific algorithms or techniques.
Browse courses on
AI Algorithms
Show steps
-
Identify specific AI algorithms or techniques that you want to learn more about.
-
Search for and find tutorials on these topics.
-
Follow the tutorials and complete the exercises.
-
Apply your learnings to your own projects or experiments.
Create a visual representation of an AI model
Show steps
Develop a deeper understanding of AI models by creating a visual representation of their structure and function.
Browse courses on
Neural Networks
Show steps
-
Choose an AI model to visualize, such as a neural network or a decision tree.
-
Identify the key components of the model and their relationships.
-
Create a visual representation of the model using a tool such as diagrams.net or draw.io.
-
Present your visualization to others and explain the model's structure and function.
Contribute to an open-source AI project
Show steps
Gain practical experience with AI by contributing to an open-source project.
Show steps
-
Identify an open-source AI project that aligns with your interests.
-
Review the project's documentation and familiarize yourself with its codebase.
-
Identify a bug or feature that you can contribute to.
-
Submit a pull request with your changes.
Review Intro to AI concepts
Show steps
Review basic concepts of AI, such as machine learning, supervised learning, unsupervised learning, and reinforcement learning.
Browse courses on
Artificial Intelligence
Show steps
Show steps
Show steps
- Read the course syllabus and skim the course content.
- Watch videos or tutorials on the basics of AI.
- Complete practice exercises on basic AI concepts.
Compile a collection of AI resources
Show steps
Organize and expand your knowledge of AI by compiling a collection of useful resources.
Show steps
Show steps
Show steps
- Identify different types of AI resources, such as articles, tutorials, books, and datasets.
- Search for and gather resources that align with your learning goals.
- Organize the resources into a structured collection, such as a folder or a shared document.
- Regularly review and update your collection as you progress in your learning.
Read and review 'Artificial Intelligence: A Modern Approach'
Show steps
Gain a comprehensive understanding of the foundational concepts and algorithms in AI.
Show steps
Show steps
Show steps
- Read the book and take notes on the key concepts.
- Complete the exercises and assignments in the book.
- Write a review of the book, summarizing your key takeaways.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice writing simple AI algorithms
Show steps
Apply your understanding of AI algorithms by writing code to implement them.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Choose a simple AI algorithm to implement, such as linear regression or decision trees.
- Implement the algorithm in a programming language of your choice.
- Test your implementation on a dataset.
- Refine your implementation based on the results of your testing.
Follow tutorials on specific AI algorithms or techniques
Show steps
Expand your knowledge of AI by following tutorials on specific algorithms or techniques.
Browse courses on
AI Algorithms
Show steps
Show steps
Show steps
- Identify specific AI algorithms or techniques that you want to learn more about.
- Search for and find tutorials on these topics.
- Follow the tutorials and complete the exercises.
- Apply your learnings to your own projects or experiments.
Create a visual representation of an AI model
Show steps
Develop a deeper understanding of AI models by creating a visual representation of their structure and function.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Choose an AI model to visualize, such as a neural network or a decision tree.
- Identify the key components of the model and their relationships.
- Create a visual representation of the model using a tool such as diagrams.net or draw.io.
- Present your visualization to others and explain the model's structure and function.
Contribute to an open-source AI project
Show steps
Gain practical experience with AI by contributing to an open-source project.
Show steps
Show steps
Show steps
- Identify an open-source AI project that aligns with your interests.
- Review the project's documentation and familiarize yourself with its codebase.
- Identify a bug or feature that you can contribute to.
- Submit a pull request with your changes.
Career center
Learners who complete Introduction to Machine Learning and AI will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract meaningful insights from data. This course can help you build a foundation in machine learning, a key skill for Data Scientists. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course can help you build a foundation in machine learning, a key skill for Machine Learning Engineers. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
AI Engineer
AI Engineer
AI Engineers are responsible for designing, developing, and deploying AI systems. This course can help you build a foundation in machine learning, a key technology for AI systems. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and deploying software applications. Machine learning is increasingly being used in software applications, so this course can help you build a foundation in machine learning, a valuable skill for Software Engineers. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data. Machine learning is increasingly being used to analyze data, so this course can help you build a foundation in machine learning, a valuable skill for Data Analysts. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and analytical techniques to solve problems in business and industry. Machine learning is increasingly being used to solve problems in business and industry, so this course can help you build a foundation in machine learning, a valuable skill for Operations Research Analysts. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Business Analyst
Business Analyst
Business Analysts are responsible for understanding and improving business processes. Machine learning is increasingly being used to improve business processes, so this course can help you build a foundation in machine learning, a valuable skill for Business Analysts. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. Machine learning is increasingly being used to analyze data, so this course can help you build a foundation in machine learning, a valuable skill for Statisticians. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Product Manager
Product Manager
Product Managers are responsible for planning and developing products. Machine learning is increasingly being used to develop products, so this course can help you build a foundation in machine learning, a valuable skill for Product Managers. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Actuary
Actuary
Actuaries are responsible for assessing and managing risk. Machine learning is increasingly being used to assess and manage risk, so this course can help you build a foundation in machine learning, a valuable skill for Actuaries. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making investment recommendations. Machine learning is increasingly being used to analyze financial data, so this course can help you build a foundation in machine learning, a valuable skill for Financial Analysts. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Market Researcher
Market Researcher
Market Researchers are responsible for collecting and analyzing data about markets and consumers. Machine learning is increasingly being used to collect and analyze data about markets and consumers, so this course can help you build a foundation in machine learning, a valuable skill for Market Researchers. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
UX Researcher
UX Researcher
UX Researchers are responsible for studying how users interact with products and services. Machine learning is increasingly being used to study how users interact with products and services, so this course can help you build a foundation in machine learning, a valuable skill for UX Researchers. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Data Journalist
Data Journalist
Data Journalists are responsible for using data to tell stories. Machine learning is increasingly being used to tell stories, so this course can help you build a foundation in machine learning, a valuable skill for Data Journalists. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
Technical Writer
Technical Writer
Technical Writers are responsible for writing documentation for software and other technical products. Machine learning is increasingly being used to develop software and other technical products, so this course can help you build a foundation in machine learning, a valuable skill for Technical Writers. You'll learn about different types of machine learning, how to collect and prepare data for machine learning, and how to train and evaluate machine learning models.
For more career information including salaries, visit:
OpenCourser.com/course/jjylku/introduction
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Machine Learning and AI.
Provides a comprehensive introduction to machine learning, covering a wide range of topics from supervised to unsupervised learning, and from regression to classification.
Provides a hands-on introduction to machine learning using popular Python libraries.
Provides a comprehensive introduction to probabilistic graphical models, which are a powerful tool for representing and reasoning about uncertainty.
Provides a thorough grounding in the mathematics that underlies machine learning.
Provides a comprehensive introduction to natural language processing, a subfield of machine learning that deals with the understanding of human language.
Provides a comprehensive introduction to speech and language processing, a subfield of machine learning that deals with the understanding of human speech.
Provides a comprehensive introduction to reinforcement learning, a subfield of machine learning that deals with the learning of optimal behavior through trial and error.
Provides a rigorous treatment of machine learning from a Bayesian and optimization perspective.
Save
Provides a comprehensive introduction to computer vision, a subfield of machine learning that deals with the understanding of images.
Gentle introduction to machine learning for beginners.
Save
Comprehensive guide to deep learning, a subfield of machine learning that has been responsible for many recent advances in AI.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jjylku/introduction
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Rating
4.5
Effort
2 to 4 hours per week for 4 weeks
Level
Elementary
Via
edX
Institution
Raspberry Pi Foundation
Instructors
Ben Garside
Mac Bowley
Language
English
Transcript
English
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