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Google AI for Anyone

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Based on 1 rating
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Laurence Moroney

Google AI for Anyone teaches you about what Artificial Intelligence is. You’ll cut through the hype and learn about AI and Machine Learning.

As its name suggests, this course is for anybody -- you don’t need a computer science, mathematics or AI background to understand it. No programming skills or prior knowledge are needed.

We’ll take you through, from first principles what the fuss is all about, and you’ll get hands-on in playing with data to teach a computer how to recognize images, sounds and more.

Read more

Google AI for Anyone teaches you about what Artificial Intelligence is. You’ll cut through the hype and learn about AI and Machine Learning.

As its name suggests, this course is for anybody -- you don’t need a computer science, mathematics or AI background to understand it. No programming skills or prior knowledge are needed.

We’ll take you through, from first principles what the fuss is all about, and you’ll get hands-on in playing with data to teach a computer how to recognize images, sounds and more.

As you explore how AI is used in the real world (recommender systems, computer vision, self-driving etc.) you will also begin to build an understanding of Neural networks and the types of machine learning including supervised, unsupervised, reinforcement etc. You will also see (and experience) what programming AI looks like and how it is applied.

From here you will be able to continue your journey through the emerging fields of AI and ML and related technologies. In so doing, you will formulate a basis to understand and discuss AI and ML related matters in your personal and professional life.

Enroll now

What's inside

Learning objectives

  • What ai is and isn’t
  • How ai, ml, deep learning all fit together
  • Why data is important
  • Applications of ai
  • What programming ai looks like - predicting numbers with regression, computer-assisted decisions with classification, gaming etc can make mistakes because of poor data
  • Neural networks -- what they are and what they aren't. basics. forward and backward propagation
  • Understand how fairness and ethics work in ai
  • The process of teaching a computer how to learn
  • How ai applications can make mistakes because of poor data

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Introduces learners to the fundamentals of Artificial Intelligence and Machine Learning, making it accessible to beginners without prior knowledge
Provides hands-on experience in training computers to recognize images, sounds, and more, empowering learners with practical skills in AI
Explodes common myths and misconceptions surrounding AI, enabling learners to develop a nuanced understanding of its capabilities and limitations
Covers a comprehensive range of AI applications in various industries, allowing learners to appreciate the practical impact of AI in the real world
Lays a solid foundation for learners to pursue further exploration in AI and related fields, providing a gateway to advanced concepts and specialized domains
Instructed by industry expert Laurence Moroney, bringing real-world insights and cutting-edge knowledge to the course content

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Create your own learning path. Save this course to your list so you can find it easily later.
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Reviews summary

Accessible introduction to ai fundamentals

According to students, this course is an excellent starting point for anyone looking to understand AI without a technical background. Learners consistently praise its ability to demystify complex concepts, breaking down topics like machine learning and deep learning into digestible, easy-to-understand modules. The inclusion of hands-on activities is highlighted as very helpful for making theory stick, and the ethical considerations chapter is frequently mentioned as insightful and crucial. While some experienced learners found the pace a bit too basic, the course largely succeeds in providing a solid foundational understanding for its target audience, preparing them for further exploration of AI.
Provides a strong base for understanding AI and further learning.
"I feel much more confident discussing AI now."
"This course demystifies AI and prepares you for more advanced topics if you choose to pursue them."
"A great foundation for further learning."
"I feel like I have a really good grasp of the fundamentals."
Practical exercises effectively reinforce theoretical concepts.
"The hands-on activities, especially with image recognition, really made the theory stick."
"I particularly appreciated the hands-on parts, which were engaging and helped illustrate the concepts."
"The hands-on exercises were simple but effective in demonstrating the concepts."
"I enjoyed the simple, yet effective, hands-on activities that made the concepts tangible."
Comprehensive coverage of fairness and ethical aspects of AI.
"The ethical considerations chapter was also very insightful."
"The fairness and ethics discussions were also very timely and relevant."
"The emphasis on ethics was also a big plus."
"The ethical content was well-presented."
Complex AI topics explained in a very understandable way.
"It breaks down complex concepts like machine learning and deep learning into digestible, easy-to-understand modules."
"The explanations are clear, and the way they explained neural networks was brilliant."
"Couldn't ask for a better entry-level AI course. It explains AI without jargon, which is exactly what I needed."
Perfect for those with no prior technical knowledge.
"This course is an excellent starting point for anyone curious about AI but feeling overwhelmed."
"As someone with zero prior knowledge, I was worried it would be too technical, but the instructor made everything incredibly accessible."
"I came into this course knowing nothing about AI, and now I feel like I have a really good grasp of the fundamentals."
"It delivered exactly what it promised: AI for anyone, without requiring a technical background."
May be too slow or high-level for those with technical backgrounds.
"If you already have a basic understanding of technology or data, you might find parts of it a bit too basic."
"As an experienced software engineer, I found this course much too basic. It's a very high-level overview."
"My only minor critique is that some parts felt a bit too high-level, and I was hoping for slightly more depth..."
"If you're looking for deep technical dives or coding, this isn't it, but it doesn't claim to be."

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 Google AI for Anyone with these activities:
Refresh Your Math and Statistics Skills
Ensure a solid foundation in math and statistics to enhance your comprehension of AI concepts.
Browse courses on Linear Algebra
Show steps
  • Review fundamental concepts in algebra, calculus, and statistics.
  • Solve practice problems to strengthen your understanding.
  • Optional: Take an online course or consult textbooks to refresh your knowledge.
Organize Your Course Materials
Stay organized and improve your learning experience by compiling and reviewing your course materials.
Show steps
  • Create a system for filing notes, assignments, and other course materials.
  • Regularly review your materials to reinforce your understanding.
  • Optional: Create summaries or mind maps to enhance your comprehension.
Read 'AI for Everyone' by Laurence Moroney
Start your AI journey by reviewing some of the fundamental concepts covered in this book.
View AI and Machine Learning for On-Device... on Amazon
Show steps
  • Acquire a copy of the book or find it online.
  • Read the first three chapters to gain an overview of AI.
  • Take notes on key concepts and definitions to enhance your understanding.
  • Optional: Engage in discussions online or with peers to clarify doubts.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Practice Problems on Machine Learning Algorithms
Reinforce your understanding of machine learning algorithms by practicing with various problems.
Browse courses on Machine Learning Algorithms
Show steps
  • Identify reputable websites or online resources that provide practice problems.
  • Choose a specific algorithm to focus on, such as linear regression or decision trees.
  • Solve several practice problems related to the chosen algorithm.
  • Analyze your solutions and identify areas for improvement.
  • Optional: Collaborate with classmates or join online forums to discuss your solutions.
Participate in Study Groups or Online Discussions
Connect with other learners to clarify concepts, share insights, and enhance your understanding.
Show steps
  • Identify online platforms or forums dedicated to AI and machine learning.
  • Join study groups or participate in discussion threads.
  • Actively engage in discussions, ask questions, and share your perspectives.
  • Optional: Organize small group study sessions with classmates to focus on specific topics.
Volunteer at an AI-Related Organization
Gain practical experience and contribute to the AI community by volunteering your time.
Show steps
  • Identify organizations involved in AI research, development, or outreach.
  • Reach out to these organizations and inquire about volunteer opportunities.
  • Participate in activities such as data collection, project assistance, or community outreach.
  • Optional: Use this experience to build connections and learn from professionals in the field.
Build a Simple AI Model Using Python or R
Apply your knowledge by building and testing a basic AI model using a programming language.
Show steps
  • Choose a dataset and define a problem statement.
  • Select an appropriate AI algorithm and implement it using Python or R.
  • Train and test your model using the chosen dataset.
  • Evaluate the performance of your model and make necessary adjustments.
  • Optional: Present your model to peers or online communities for feedback.
Start a Personal AI Project
Challenge yourself by initiating a personal project that allows you to apply AI concepts in a practical setting.
Show steps
  • Identify a real-world problem that AI can potentially address.
  • Gather data and explore different AI techniques that could be applied.
  • Develop a plan for implementing your project, including the choice of algorithms and tools.
  • Implement your project and test its performance.
  • Optional: Share your project with others or publish it on code-sharing platforms.

Career center

Learners who complete Google AI for Anyone will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
As a Machine Learning Engineer, you will build, deploy, and maintain machine learning models. To do this well, you must have a strong understanding of the underlying concepts of machine learning. Google AI for Anyone provides a comprehensive overview of these topics, from the basics of AI and machine learning to more advanced topics such as neural networks and deep learning. Taking this course will give you the knowledge and skills you need to succeed as a Machine Learning Engineer.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
Data Scientists use data to solve business problems. To do this, they need to have a strong understanding of both data analysis and machine learning. Google AI for Anyone provides a solid foundation in both of these areas. The course will teach you how to collect, clean, and analyze data, as well as how to build and deploy machine learning models. This knowledge and skills will give you a competitive edge in the job market and help you succeed as a Data Scientist.
See salaries and explore the career path for Data Scientist
Software Engineer
Software Engineers design, develop, and maintain software applications. To do this, they need to have a strong understanding of computer science fundamentals, including data structures, algorithms, and software design. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Software Engineer.
See salaries and explore the career path for Software Engineer
Product Manager
Product Managers are responsible for developing and launching new products. To do this, they need to have a deep understanding of both the technical and business aspects of product development. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Product Manager.
See salaries and explore the career path for Product Manager
Business Analyst
Business Analysts use data to identify and solve business problems. To do this, they need to have a strong understanding of both data analysis and business principles. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Business Analyst.
See salaries and explore the career path for Business Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. To do this, they need to have a strong understanding of both data analysis and statistics. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Data Analyst.
See salaries and explore the career path for Data Analyst
Market Researcher
Market Researchers use data to understand consumer behavior. To do this, they need to have a strong understanding of both data analysis and market research techniques. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Market Researcher.
See salaries and explore the career path for Market Researcher
Financial Analyst
Financial Analysts use data to make investment decisions. To do this, they need to have a strong understanding of both financial analysis and data analysis. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Financial Analyst.
See salaries and explore the career path for Financial Analyst
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency of operations. To do this, they need to have a strong understanding of both operations research and data analysis. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as an Operations Research Analyst.
See salaries and explore the career path for Operations Research Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. To do this, they need to have a strong understanding of both financial analysis and data analysis. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Quantitative Analyst.
See salaries and explore the career path for Quantitative Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. To do this, they need to have a strong understanding of both risk management and data analysis. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Risk Analyst.
See salaries and explore the career path for Risk Analyst
Statistician
Statisticians use data to collect, analyze, and interpret data. To do this, they need to have a strong understanding of both statistics and data analysis. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Statistician.
See salaries and explore the career path for Statistician
Teacher
Teachers use data to track student progress and identify areas where students need additional support. To do this, they need to have a strong understanding of both data analysis and education. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Teacher.
See salaries and explore the career path for Teacher
Journalist
Journalists use data to research and write stories. To do this, they need to have a strong understanding of both data analysis and journalism. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Journalist.
See salaries and explore the career path for Journalist
Consultant
Consultants use data to help businesses solve problems. To do this, they need to have a strong understanding of both data analysis and business. Google AI for Anyone provides a good overview of these topics, as well as an introduction to artificial intelligence and machine learning. Taking this course will give you the knowledge and skills you need to succeed as a Consultant.
See salaries and explore the career path for Consultant

Reading list

We've selected nine 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 Google AI for Anyone.
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning, covering the latest algorithms and techniques. It valuable resource for anyone interested in learning more about deep learning and how it is used to solve real-world problems.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a practical introduction to machine learning using Python, with a focus on the popular Scikit-Learn, Keras, and TensorFlow libraries. It great resource for anyone who wants to learn more about machine learning and how to use it to solve real-world problems.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Reinforcement Learning, second edition
Save
Provides a comprehensive overview of reinforcement learning, covering the latest algorithms and techniques. It valuable resource for anyone interested in learning more about reinforcement learning and how it is used to solve real-world problems.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Speech and Language Processing. An Introduction to...
Save
Provides a comprehensive overview of speech and language processing, covering the latest algorithms and techniques. It valuable resource for anyone interested in learning more about speech and language processing and how it is used to solve real-world problems.
Speech and Language Processing An Introduction to...
Paperback
$$
Cover image
Probabilistic Robotics
Save
Provides a comprehensive overview of probabilistic robotics, covering the latest algorithms and techniques. It valuable resource for anyone interested in learning more about probabilistic robotics and how it is used to solve real-world problems.
Probabilistic Robotics (Intelligent Robotics and...
Hardcover
$$$
Probabilistic Robotics (Intelligent Robotics and...
Kindle Edition
$$$
Cover image
Deep Learning for Coders with fastai and PyTorch
Save
Provides a comprehensive overview of deep learning using the Fastai and PyTorch libraries. It great resource for anyone who wants to learn more about deep learning and how to use it to solve real-world problems.
Deep Learning for Coders with Fastai and PyTorch:...
Paperback
$$
Deep Learning for Coders with fastai and PyTorch:...
Kindle Edition
$$
Cover image
Deep Learning with Python, Second Edition
Save
Provides a comprehensive overview of TensorFlow, a popular open-source library for deep learning. It great resource for anyone who wants to learn more about TensorFlow and how to use it to solve real-world problems.
Deep Learning with Python, Second Edition
Paperback
$$
Deep Learning with Python, Second Edition
Kindle Edition
$$
Cover image
Deep Learning with TensorFlow and Keras
Save
Provides a comprehensive overview of Keras, a popular high-level neural networks API, written in Python and capable of running on top of TensorFlow or Theano. It great resource for anyone who wants to learn more about Keras and how to use it to solve real-world problems.
Deep Learning with TensorFlow and Keras - Third...
Paperback
$$
Deep Learning with TensorFlow 2 and Keras - Second...
Paperback
$$
Deep Learning with TensorFlow and Keras: Build and...
Kindle Edition
$$
Deep Learning with TensorFlow 2 and Keras:...
Kindle Edition
$$
Cover image
MACHINE LEARNING
Save
Provides a comprehensive overview of machine learning, covering the latest algorithms and techniques. It great resource for anyone who wants to learn more about machine learning and how to use it to solve real-world problems.
Machine Learning: An Algorithmic Perspective,...
Hardcover
$$$
Machine Learning: An Algorithmic Perspective...
Paperback
$$
An Algorithmic Perspective on Machine Learning (2nd...
Paperback
$$$
Machine Learning: An Algorithmic Perspective,...
Kindle Edition
$$$

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Course image
Rating
5.0 Filled star Filled star Filled star Filled star Filled star
Effort
2 to 3 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Google
Instructor
Laurence Moroney
Language
English
Transcript
English
This course belongs to a collection called Fundamentals of Google AI for Web Based Machine Learning. It's comprised of two courses:
  1. Google AI for JavaScript developers with TensorFlow.js
  2. Google AI for Anyone (viewing)
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Introduces learners to the fundamentals of Artificial Intelligence and Machine Learning, making it accessible to beginners without prior knowledge
Provides hands-on experience in training computers to recognize images, sounds, and more, empowering learners with practical skills in AI
Explodes common myths and misconceptions surrounding AI, enabling learners to develop a nuanced understanding of its capabilities and limitations
Covers a comprehensive range of AI applications in various industries, allowing learners to appreciate the practical impact of AI in the real world
Lays a solid foundation for learners to pursue further exploration in AI and related fields, providing a gateway to advanced concepts and specialized domains
Instructed by industry expert Laurence Moroney, bringing real-world insights and cutting-edge knowledge to the course content
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