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Google AI for Anyone
Laurence Moroney
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.
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.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/vqcop8/google
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
- 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
Chapter 1.1 - What is AI?
Chapter 1.2 - Let's talk terminology and understand what AI, ML, DL are all about
Chapter 1-3 Ethics and Fairness
Good to know
Good to know
Know what's good ,
what to watch for , 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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Reviews summary
Intro to ai and ml
According to the review, this introductory course on Artificial Intelligence (AI) is designed for students with no prior knowledge. It is taught by Google AI experts and can serve as the first step in a certificate program on AI for web-based machine learning. Additionally, students say that the course is easy to understand because it avoids complex technical terms.
Stepping stone to a certificate program
"Part of a certificate program: This course can be a stepping stone to a more comprehensive professional certificate on AI for Web Based Machine Learning."
Taught by Google AI professionals
"Taught by Google AI professionals, providing credibility and real-world insight."
Introductory AI/ML course
"The course is designed for anyone, so it avoids complex technical jargon."
"If you're curious about AI and ML but have no background, this course is a great place to 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 Google AI for Anyone with these
activities:
Refresh Your Math and Statistics Skills
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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.
Refresh Your Math and Statistics Skills
Show steps
Ensure a solid foundation in math and statistics to enhance your comprehension of AI concepts.
Browse courses on
Linear Algebra
Show steps
Show steps
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
Show steps
Stay organized and improve your learning experience by compiling and reviewing your course materials.
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Reinforce your understanding of machine learning algorithms by practicing with various problems.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
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
Show steps
Connect with other learners to clarify concepts, share insights, and enhance your understanding.
Show steps
Show steps
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
Show steps
Gain practical experience and contribute to the AI community by volunteering your time.
Show steps
Show steps
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
Show steps
Apply your knowledge by building and testing a basic AI model using a programming language.
Show steps
Show steps
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
Show steps
Challenge yourself by initiating a personal project that allows you to apply AI concepts in a practical setting.
Show steps
Show steps
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
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.
Data Scientist
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.
Software Engineer
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.
Product Manager
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.
Business Analyst
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.
Data 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.
Market Researcher
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.
Financial Analyst
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.
Operations Research 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.
Quantitative 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.
Risk 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.
Statistician
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.
Teacher
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.
Journalist
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.
Consultant
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.
For more career information including salaries, visit:
OpenCourser.com/course/vqcop8/google
Reading list
We've selected ten 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.
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.
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.
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.
Save
Provides a comprehensive overview of computer vision, covering the latest algorithms and techniques. It valuable resource for anyone interested in learning more about computer vision and how it is used to solve real-world problems.
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.
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.
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.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vqcop8/google
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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:
- Google AI for JavaScript developers with TensorFlow.js
- Google AI for Anyone (viewing)
Good to know
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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