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Google Cloud Training
Unlock the transformative power of AI and ML with Google Cloud. Explore how organizations leverage AI and ML to transform business processes. Learn online with Udacity.
Register for this course and see more details by visiting:
OpenCourser.com/course/wcw2n2/innovating
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What's inside
Syllabus
In this introduction, you'll explore the course goals and preview each section.
In this section of the course, you'll explore many of those fundamental concepts.
Read more
Syllabus
In this introduction, you'll explore the course goals and preview each section.
In this section of the course, you'll explore many of those fundamental concepts.
Read more
In this section of the course, you'll explore four options to build ML models with Google Cloud: BigQuery ML, pre-trained APIs, AutoML, and custom training.
The course closes with a summary of the key points covered in each section and next steps to continue learning.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops fundamental concepts of machine learning (ML), which is standard in many industries
Describes various options and approaches to building ML models with Google Cloud, providing learners with a comprehensive understanding
Explores the transformative potential of artificial intelligence (AI) and ML, which is highly relevant in business and industry
Taught by Google Cloud Training, who are recognized for their work in AI and ML
Provides a strong foundation for beginners in AI and ML
Save this course
Save Innovating with Google Cloud Artificial Intelligence to your list so you can find it easily later:
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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 Innovating with Google Cloud Artificial Intelligence with these
activities:
Review basic probability theory
Show steps
Concepts such as conditional probability, Bayes' theorem, and random variables are essential for understanding machine learning algorithms.
Browse courses on
Probability Theory
Show steps
-
Revisit materials from a previous course on probability theory.
-
Work through practice problems and exercises to reinforce your understanding.
Review fundamentals of Machine Learning
Show steps
Review the foundational concepts of machine learning to ensure a strong understanding before beginning the course.
Browse courses on
Machine Learning
Show steps
-
Review key concepts of linear regression.
-
Review key concepts of logistic regression.
-
Review key concepts of classification and regression algorithms.
Attend online meetups or conferences related to ML
Show steps
Connect with professionals in the ML field, exchange ideas, and stay updated on industry trends and advancements.
Browse courses on
Machine Learning
Show steps
-
Identify relevant ML meetups or conferences.
-
Attend the events and actively participate in discussions.
-
Network with attendees and learn from their experiences.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Join a machine learning study group
Show steps
Engaging with peers in a study group can provide valuable insights, support, and motivation.
Show steps
-
Find or create a study group with other students taking the course.
-
Meet regularly to discuss course materials, work on practice problems, and share ideas.
Practice building ML models in BigQuery ML
Show steps
Gain hands-on experience in building and deploying ML models using BigQuery ML, a platform designed for scalable data analysis.
Browse courses on
BigQuery ML
Show steps
-
Create a BigQuery ML model using a provided dataset.
-
Train and evaluate the model, monitoring its performance.
-
Deploy the model and make predictions on new data.
Solve ML algorithm exercises
Show steps
Applying machine learning algorithms to different types of problems will deepen your understanding of their strengths and weaknesses.
Browse courses on
Machine Learning Algorithms
Show steps
-
Find online resources or textbooks with practice exercises.
-
Work through the exercises, implementing the algorithms in a programming language of your choice.
-
Compare your solutions with others or ask for feedback from a mentor or instructor.
Build a simple image classification model using AutoML
Show steps
Apply practical, hands-on learning by building an image classification model using AutoML, a tool that allows for quick and efficient model development.
Browse courses on
AutoML
Show steps
-
Gather a dataset of images for your chosen classification task.
-
Create an AutoML project and dataset.
-
Train and evaluate the model, monitoring its performance.
-
Deploy the model and test its accuracy on unseen data.
Participate in an online workshop on ML best practices
Show steps
Enhance your ML knowledge and skills by participating in an online workshop, gaining practical insights and best practices from industry experts.
Browse courses on
Machine Learning
Show steps
-
Research and identify suitable ML workshops.
-
Register and actively participate in the workshop.
-
Apply the knowledge and techniques learned in your ML projects.
Explore online tutorials on advanced ML techniques
Show steps
Expand your knowledge and explore advanced ML techniques through carefully curated online tutorials, reinforcing your understanding and broadening your skillset.
Browse courses on
Machine Learning
Show steps
-
Identify reputable sources for ML tutorials.
-
Choose tutorials that align with your learning goals.
-
Follow the tutorials thoroughly, practicing the techniques and concepts presented.
Follow online tutorials on advanced machine learning techniques
Show steps
Exploring advanced machine learning techniques through guided tutorials will expand your knowledge and skills beyond the course content.
Show steps
-
Identify specific machine learning techniques that you want to learn more about.
-
Find reputable online tutorials or courses that cover these techniques.
-
Follow the tutorials step-by-step and implement the techniques in your own projects.
Develop a machine learning project proposal
Show steps
Creating a project proposal will help you define the scope, goals, and methodology of your machine learning project.
Show steps
-
Identify a real-world problem or dataset that you want to address with machine learning.
-
Research and select appropriate machine learning algorithms for your project.
-
Outline the steps involved in data collection, preprocessing, model training, and evaluation.
-
Write a clear and concise project proposal that outlines your plan and expected outcomes.
Develop a presentation on a chosen ML application
Show steps
Synthesize your understanding of ML applications by creating a presentation on a specific industry use case, showcasing your knowledge and communication skills.
Browse courses on
Machine Learning
Show steps
-
Research and select an ML application in a specific industry.
-
Gather data and insights to support your presentation.
-
Develop a clear and concise presentation outlining the problem, solution, and impact of the ML application.
Contribute to open-source machine learning projects
Show steps
Contributing to open-source projects can provide hands-on experience and connect you with a community of machine learning practitioners.
Browse courses on
Collaboration
Show steps
-
Find open-source machine learning projects that align with your interests.
-
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
-
Submit your contributions and engage with the project community.
Review basic probability theory
Show steps
Concepts such as conditional probability, Bayes' theorem, and random variables are essential for understanding machine learning algorithms.
Browse courses on
Probability Theory
Show steps
Show steps
Show steps
- Revisit materials from a previous course on probability theory.
- Work through practice problems and exercises to reinforce your understanding.
Review fundamentals of Machine Learning
Show steps
Review the foundational concepts of machine learning to ensure a strong understanding before beginning the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review key concepts of linear regression.
- Review key concepts of logistic regression.
- Review key concepts of classification and regression algorithms.
Attend online meetups or conferences related to ML
Show steps
Connect with professionals in the ML field, exchange ideas, and stay updated on industry trends and advancements.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify relevant ML meetups or conferences.
- Attend the events and actively participate in discussions.
- Network with attendees and learn from their experiences.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Join a machine learning study group
Show steps
Engaging with peers in a study group can provide valuable insights, support, and motivation.
Show steps
Show steps
Show steps
- Find or create a study group with other students taking the course.
- Meet regularly to discuss course materials, work on practice problems, and share ideas.
Practice building ML models in BigQuery ML
Show steps
Gain hands-on experience in building and deploying ML models using BigQuery ML, a platform designed for scalable data analysis.
Browse courses on
BigQuery ML
Show steps
Show steps
Show steps
- Create a BigQuery ML model using a provided dataset.
- Train and evaluate the model, monitoring its performance.
- Deploy the model and make predictions on new data.
Solve ML algorithm exercises
Show steps
Applying machine learning algorithms to different types of problems will deepen your understanding of their strengths and weaknesses.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Find online resources or textbooks with practice exercises.
- Work through the exercises, implementing the algorithms in a programming language of your choice.
- Compare your solutions with others or ask for feedback from a mentor or instructor.
Build a simple image classification model using AutoML
Show steps
Apply practical, hands-on learning by building an image classification model using AutoML, a tool that allows for quick and efficient model development.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Gather a dataset of images for your chosen classification task.
- Create an AutoML project and dataset.
- Train and evaluate the model, monitoring its performance.
- Deploy the model and test its accuracy on unseen data.
Participate in an online workshop on ML best practices
Show steps
Enhance your ML knowledge and skills by participating in an online workshop, gaining practical insights and best practices from industry experts.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research and identify suitable ML workshops.
- Register and actively participate in the workshop.
- Apply the knowledge and techniques learned in your ML projects.
Explore online tutorials on advanced ML techniques
Show steps
Expand your knowledge and explore advanced ML techniques through carefully curated online tutorials, reinforcing your understanding and broadening your skillset.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify reputable sources for ML tutorials.
- Choose tutorials that align with your learning goals.
- Follow the tutorials thoroughly, practicing the techniques and concepts presented.
Follow online tutorials on advanced machine learning techniques
Show steps
Exploring advanced machine learning techniques through guided tutorials will expand your knowledge and skills beyond the course content.
Show steps
Show steps
Show steps
- Identify specific machine learning techniques that you want to learn more about.
- Find reputable online tutorials or courses that cover these techniques.
- Follow the tutorials step-by-step and implement the techniques in your own projects.
Develop a machine learning project proposal
Show steps
Creating a project proposal will help you define the scope, goals, and methodology of your machine learning project.
Show steps
Show steps
Show steps
- Identify a real-world problem or dataset that you want to address with machine learning.
- Research and select appropriate machine learning algorithms for your project.
- Outline the steps involved in data collection, preprocessing, model training, and evaluation.
- Write a clear and concise project proposal that outlines your plan and expected outcomes.
Develop a presentation on a chosen ML application
Show steps
Synthesize your understanding of ML applications by creating a presentation on a specific industry use case, showcasing your knowledge and communication skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research and select an ML application in a specific industry.
- Gather data and insights to support your presentation.
- Develop a clear and concise presentation outlining the problem, solution, and impact of the ML application.
Contribute to open-source machine learning projects
Show steps
Contributing to open-source projects can provide hands-on experience and connect you with a community of machine learning practitioners.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Find open-source machine learning projects that align with your interests.
- Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
- Submit your contributions and engage with the project community.
Career center
Learners who complete Innovating with Google Cloud Artificial Intelligence will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, implement, maintain, and manage AI systems. This course will be useful to Artificial Intelligence Engineers, particularly those working on business process automation. It introduces fundamental concepts of AI and ML, and four options to build ML models with Google Cloud.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers blend software engineering and AI. A Machine Learning Engineer will apply the course's instruction on how to build ML models, and how AI and ML can transform business processes to their work. This course may be useful to Machine Learning Engineers who are just starting out, or who feel they need to strengthen their theoretical and practical foundation in AI and ML.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. Data Scientists may find this course particularly useful because of its emphasis on using AI and ML to solve real-world problems.
AI Researcher
AI Researcher
AI Researchers explore and develop new AI and ML algorithms and technologies. This course may be useful for AI Researchers who are interested in applying AI and ML to solve business problems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course may be useful for Operations Research Analysts who want to learn how to use AI and ML to solve business problems.
Business Analyst
Business Analyst
Business Analysts work with stakeholders to identify business needs and opportunities, and then develop solutions to address those needs. This course will be useful to Business Analysts who want to leverage AI and ML technologies to improve business processes and outcomes.
Product Analyst
Product Analyst
Product Analysts define, prioritize, and manage the development of products that meet the needs of users. Product Analysts may find this course particularly useful because of its focus on using AI and ML to drive product development decisions.
Product Manager
Product Manager
Product Managers are responsible for the end-to-end development and management of products. This course may be useful for Product Managers who want to incorporate AI and ML into their products.
Data Engineer
Data Engineer
Data Engineers design, implement, and maintain data pipelines and systems. This course may be useful for Data Engineers who want to learn how to incorporate AI and ML into their work.
Systems Analyst
Systems Analyst
Systems Analysts design, develop, and maintain computer systems. This course may be useful for Systems Analysts who want to learn how to incorporate AI and ML into their systems.
IT Architect
IT Architect
IT Architects design, develop, and manage IT systems. This course may be useful for IT Architects who want to learn how to incorporate AI and ML into their systems.
Full-Stack Developer
Full-Stack Developer
Full Stack Developers design, develop, and maintain both the front-end and back-end of web applications. This course may be useful for Full Stack Developers who want to learn how to build AI and ML-powered web applications.
Data Analyst
Data Analyst
Data Analysts analyze data to identify patterns, trends, and other useful information. Those working in AI or ML may find this course useful, particularly the parts on how to build ML models with Google Cloud.
Software Engineer
Software Engineer
Software Engineers apply software engineering principles and programming languages to the design, development, maintenance, testing, and evaluation of computer software. This course could be useful for Software Engineers who wish to add to their skillset and who are interested in developing new applications of AI and ML for business.
Management Consultant
Management Consultant
Management Consultants help businesses improve their performance by identifying and solving problems. This course could be useful for Management Consultants who want to learn how to use AI and ML to help their clients solve business problems.
For more career information including salaries, visit:
OpenCourser.com/course/wcw2n2/innovating
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
Innovating with Google Cloud Artificial Intelligence.
This practical guide offers hands-on experience with Python libraries for machine learning, providing valuable insights into model building and evaluation.
This hands-on guide focuses on practical implementation of machine learning models using popular Python libraries, providing valuable experience for students.
This practical guide focuses on implementing deep learning models using Fastai and PyTorch, providing hands-on experience with code.
This practical guide explores real-world applications of AI, providing insights into its potential impact across various industries.
This advanced textbook provides a comprehensive overview of statistical learning methods, offering a deeper understanding of AI algorithms.
Save
This comprehensive textbook covers the fundamentals of computer vision, offering a deeper understanding of image processing and analysis techniques.
This foundational textbook on reinforcement learning provides insights into the underlying principles and algorithms, suitable for readers with a strong technical background.
This advanced textbook presents a mathematical and statistical approach to machine learning, suitable for readers with a strong mathematical background.
Save
This advanced textbook covers deep learning theory and algorithms, offering a more comprehensive understanding of AI models for readers with a strong technical background.
This advanced textbook provides a comprehensive overview of probabilistic graphical models, offering a deeper understanding of AI algorithms for reasoning and inference.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wcw2n2/innovating
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Effort
1 hour
Via
Udacity
Instructor
Google Cloud Training
Language
English
Good to know
Develops fundamental concepts of machine learning (ML), which is standard in many industries
Describes various options and approaches to building ML models with Google Cloud, providing learners with a comprehensive understanding
Explores the transformative potential of artificial intelligence (AI) and ML, which is highly relevant in business and industry
Taught by Google Cloud Training, who are recognized for their work in AI and ML
Provides a strong foundation for beginners in AI and ML
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