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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. In this lab you train and deploy a TensorFlow model to AI Platform for serving (prediction). Watch these short videos Harness the Power of Machine Learning with AI Platform and AI Platform: Qwik Start - Qwiklabs Preview.
Register for this course and see more details by visiting:
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What's inside
Syllabus
AI Platform: Qwik Start
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Exposes learners to TensorFlow, a popular machine learning library, which is standard in the data science industry
Teaches learners how to deploy machine learning models to AI Platform for serving (prediction), a valuable skill in modern data science
Offers self-paced labs, a convenient and flexible learning format that accommodates diverse learner schedules
Save this course
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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 AI Platform: Qwik Start with these
activities:
Review supervised machine learning concepts
Show steps
Refreshes supervised machine learning to strengthen the foundational knowledge needed for this course.
Browse courses on
Supervised Machine Learning
Show steps
-
Revisit and summarize fundamental concepts of supervised learning
-
Practice and implement algorithms such as regression and classification
-
Review the benefits and limitations of supervised learning
Review Deep Learning
Show steps
Review the basics of deep learning and TensorFlow to prepare for this course's content.
Browse courses on
Deep Learning
Show steps
-
Learn about the basics of deep learning concepts like neural networks and backpropagation.
-
Get familiar with implementing deep learning models using TensorFlow.
-
Practice building and training simple deep learning models using the TensorFlow library.
Complete Google Cloud Fundamentals Training - Machine Learning
Show steps
Reinforces and deepens understanding of machine learning principles, offering practical guidance to complement theoretical concepts.
Show steps
-
Follow the interactive tutorial and complete each module
-
Review and apply concepts covered in the tutorial, such as machine learning models and algorithms
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Join a study group or discussion forum
Show steps
Facilitates peer-to-peer learning, enabling students to share knowledge, discuss concepts, and clarify doubts.
Show steps
-
Find or create a study group with peers enrolled in the same course
-
Regularly meet to discuss course material, share insights, and work on assignments together
Solve practice problems on Kaggle
Show steps
Provides hands-on experience in applying machine learning techniques to real-world datasets, enhancing problem-solving skills.
Show steps
-
Select a Kaggle competition or dataset relevant to the course material
-
Study the problem and explore available data
-
Develop and implement machine learning models to address the problem
-
Evaluate and refine models to improve accuracy and performance
Practice Exercises
Show steps
Complete practice exercises to reinforce your understanding of the concepts and techniques covered in this course.
Browse courses on
AI Platform
Show steps
-
Solve practice problems related to AI Platform, TensorFlow, and machine learning.
-
Work through code exercises to implement machine learning models.
-
Test your knowledge and identify areas where you need further improvement.
TensorFlow Tutorials
Show steps
Follow these tutorials to gain practical experience with TensorFlow and reinforce your understanding of the concepts covered in this course.
Browse courses on
TensorFlow
Show steps
-
Follow the official TensorFlow tutorials to get started with the library.
-
Work through examples to learn how to build and train machine learning models.
-
Experiment with different TensorFlow modules to explore its capabilities.
Build a personal machine learning project
Show steps
Encourages students to apply their knowledge by creating their own projects, fostering creativity, and reinforcing concepts.
Show steps
-
Identify a problem or topic of interest that aligns with the course material
-
Gather and prepare a dataset relevant to the project
-
Develop and train a machine learning model to address the problem
-
Evaluate and refine the model, focusing on accuracy and performance
-
Document and present the project findings
Study Groups
Show steps
Join study groups to collaborate with peers, discuss course concepts, and work on assignments together.
Show steps
-
Find or form a study group with other students enrolled in the course.
-
Meet regularly to review course material, work on assignments, and support each other's learning.
-
Facilitate discussions, share resources, and provide feedback to enhance understanding.
Course Summary
Show steps
Summarize the key concepts and techniques learned in this course to solidify your understanding.
Show steps
-
Review the course materials and identify the main concepts and ideas.
-
Create a written summary, presentation, or infographic that outlines the key points.
-
Share your summary with others to reinforce your learning and contribute to the community.
Machine Learning Project
Show steps
Apply the skills and knowledge gained in this course to a practical machine learning project.
Show steps
-
Define a machine learning problem and gather relevant data.
-
Build and train a machine learning model.
-
Evaluate the performance of the model and make improvements.
-
Deploy the model and monitor its performance.
Become a Course Mentor
Show steps
Share your knowledge and support others by becoming a course mentor.
Show steps
-
Apply to become a course mentor and complete the required training.
-
Provide guidance and support to students in the course.
-
Facilitate discussions, answer questions, and contribute to the course community.
Review supervised machine learning concepts
Show steps
Refreshes supervised machine learning to strengthen the foundational knowledge needed for this course.
Browse courses on
Supervised Machine Learning
Show steps
Show steps
Show steps
- Revisit and summarize fundamental concepts of supervised learning
- Practice and implement algorithms such as regression and classification
- Review the benefits and limitations of supervised learning
Review Deep Learning
Show steps
Review the basics of deep learning and TensorFlow to prepare for this course's content.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Learn about the basics of deep learning concepts like neural networks and backpropagation.
- Get familiar with implementing deep learning models using TensorFlow.
- Practice building and training simple deep learning models using the TensorFlow library.
Complete Google Cloud Fundamentals Training - Machine Learning
Show steps
Reinforces and deepens understanding of machine learning principles, offering practical guidance to complement theoretical concepts.
Show steps
Show steps
Show steps
- Follow the interactive tutorial and complete each module
- Review and apply concepts covered in the tutorial, such as machine learning models and algorithms
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Join a study group or discussion forum
Show steps
Facilitates peer-to-peer learning, enabling students to share knowledge, discuss concepts, and clarify doubts.
Show steps
Show steps
Show steps
- Find or create a study group with peers enrolled in the same course
- Regularly meet to discuss course material, share insights, and work on assignments together
Solve practice problems on Kaggle
Show steps
Provides hands-on experience in applying machine learning techniques to real-world datasets, enhancing problem-solving skills.
Show steps
Show steps
Show steps
- Select a Kaggle competition or dataset relevant to the course material
- Study the problem and explore available data
- Develop and implement machine learning models to address the problem
- Evaluate and refine models to improve accuracy and performance
Practice Exercises
Show steps
Complete practice exercises to reinforce your understanding of the concepts and techniques covered in this course.
Browse courses on
AI Platform
Show steps
Show steps
Show steps
- Solve practice problems related to AI Platform, TensorFlow, and machine learning.
- Work through code exercises to implement machine learning models.
- Test your knowledge and identify areas where you need further improvement.
TensorFlow Tutorials
Show steps
Follow these tutorials to gain practical experience with TensorFlow and reinforce your understanding of the concepts covered in this course.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow the official TensorFlow tutorials to get started with the library.
- Work through examples to learn how to build and train machine learning models.
- Experiment with different TensorFlow modules to explore its capabilities.
Build a personal machine learning project
Show steps
Encourages students to apply their knowledge by creating their own projects, fostering creativity, and reinforcing concepts.
Show steps
Show steps
Show steps
- Identify a problem or topic of interest that aligns with the course material
- Gather and prepare a dataset relevant to the project
- Develop and train a machine learning model to address the problem
- Evaluate and refine the model, focusing on accuracy and performance
- Document and present the project findings
Study Groups
Show steps
Join study groups to collaborate with peers, discuss course concepts, and work on assignments together.
Show steps
Show steps
Show steps
- Find or form a study group with other students enrolled in the course.
- Meet regularly to review course material, work on assignments, and support each other's learning.
- Facilitate discussions, share resources, and provide feedback to enhance understanding.
Course Summary
Show steps
Summarize the key concepts and techniques learned in this course to solidify your understanding.
Show steps
Show steps
Show steps
- Review the course materials and identify the main concepts and ideas.
- Create a written summary, presentation, or infographic that outlines the key points.
- Share your summary with others to reinforce your learning and contribute to the community.
Machine Learning Project
Show steps
Apply the skills and knowledge gained in this course to a practical machine learning project.
Show steps
Show steps
Show steps
- Define a machine learning problem and gather relevant data.
- Build and train a machine learning model.
- Evaluate the performance of the model and make improvements.
- Deploy the model and monitor its performance.
Become a Course Mentor
Show steps
Share your knowledge and support others by becoming a course mentor.
Show steps
Show steps
Show steps
- Apply to become a course mentor and complete the required training.
- Provide guidance and support to students in the course.
- Facilitate discussions, answer questions, and contribute to the course community.
Career center
Learners who complete AI Platform: Qwik Start will develop knowledge and skills
that may be useful to these careers:
AI Engineer
AI Engineer
AI Engineers design, build, and maintain AI systems and applications. They work with data scientists and machine learning engineers to bring AI solutions to life. AI Platform: Qwik Start helps build a foundation for aspiring AI Engineers by introducing fundamental concepts for training and deploying prediction models.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing systems. They work with businesses to migrate their applications and data to the cloud. AI Platform: Qwik Start helps build a foundation for aspiring Cloud Architects by introducing foundational concepts for deploying AI models in the cloud.
Product Manager
Product Manager
Product Managers plan and oversee the development of products. They work with engineers, designers, and other stakeholders to ensure that products meet the needs of users. AI Platform: Qwik Start helps build a foundation for aspiring Product Managers who wish to develop AI-powered products by introducing concepts for training and deploying prediction models.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines that collect, store, and process data. AI Platform: Qwik Start may be useful for Data Engineers by introducing foundational concepts for training and deploying prediction models that can be used in data pipelines.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Many Software Engineers specialize in developing AI-powered applications. AI Platform: Qwik Start could help Software Engineers gain the fundamental knowledge necessary to develop and deploy models for prediction.
DevOps Engineer
DevOps Engineer
DevOps Engineers work to bridge the gap between development and operations teams. They ensure that software is deployed and maintained efficiently. AI Platform: Qwik Start could help DevOps Engineers gain foundational knowledge for working with AI models in production.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risks for businesses. AI Platform: Qwik Start may be useful for Risk Analysts by introducing foundational concepts for training and deploying prediction models that can be used to assess and manage risks.
Data Scientist
Data Scientist
In the role of Data Scientist, one would collect and analyze both structured and unstructured data. Using this data, they make predictions and create models that solve business problems. AI Platform: Qwik Start may be useful for a Data Scientist because it introduces foundational concepts for training and deploying prediction models.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve complex business challenges. AI Platform: Qwik Start may be useful for a Machine Learning Engineer by introducing foundational concepts for training and deploying prediction models.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data and make recommendations for investments and other financial decisions. AI Platform: Qwik Start may be useful for Financial Analysts by introducing foundational concepts for training and deploying prediction models that can be used to analyze financial data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make predictions. AI Platform: Qwik Start may be useful for Quantitative Analysts by introducing foundational concepts for training and deploying prediction models.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage risks for insurance companies and other financial institutions. AI Platform: Qwik Start may be useful for Actuaries by introducing foundational concepts for training and deploying prediction models that can be used to assess and manage risks.
Business Analyst
Business Analyst
Business Analysts help businesses understand their data and make better decisions. AI Platform: Qwik Start could help aspiring Business Analysts learn the basics of training and deploying AI models for solving business problems.
Project Manager
Project Manager
Project Managers plan and oversee the execution of projects. They work with stakeholders to define project scope, timelines, and budgets. AI Platform: Qwik Start could be useful for Project Managers who are working on AI-related projects by introducing foundational concepts for training and deploying prediction models.
Data Analyst
Data Analyst
Data Analysts clean, analyze, and interpret data to help businesses make informed decisions. AI Platform: Qwik Start may be useful to Data Analysts by introducing foundational concepts for training and deploying models that help automate parts of their workflow.
For more career information including salaries, visit:
OpenCourser.com/course/1v8ifo/ai
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
AI Platform: Qwik Start.
Save
Comprehensive reference for deep learning, covering both theoretical and practical aspects. It provides a detailed overview of deep learning architectures, training algorithms, and applications, offering a thorough foundation for understanding the field and its relevance to AI Platform.
Offers a comprehensive treatment of pattern recognition and machine learning, covering both theoretical foundations and practical applications. It provides a thorough understanding of the underlying principles and algorithms used in AI Platform, offering a deep dive into the field.
Classic reference for statistical learning, providing a comprehensive overview of the field. It covers both theoretical foundations and practical applications, offering insights into the statistical principles and techniques used in AI Platform, making it a valuable resource for understanding the underlying statistical concepts.
Offers a comprehensive overview of artificial intelligence (AI), covering a wide range of topics from foundations to advanced techniques. It provides a comprehensive introduction to the field of AI, offering a broad understanding of the concepts and principles that underpin AI Platform.
Presents a probabilistic approach to machine learning, emphasizing the mathematical foundations and statistical principles behind machine learning models. It provides a comprehensive treatment of probabilistic modeling and inference, offering a deeper understanding of AI Platform's underlying probabilistic nature.
Focuses specifically on deep learning and provides detailed guidance on building and training neural networks using TensorFlow. It includes numerous code examples and practical insights, making it a valuable resource for understanding the underlying principles of deep learning.
Provides a comprehensive introduction to Bayesian reasoning and its applications in machine learning. It covers the theoretical foundations of Bayesian probability, as well as practical techniques for building and evaluating Bayesian models, offering a deeper understanding of the probabilistic underpinnings of AI Platform.
Offers a practical approach to machine learning, covering a wide range of topics from data manipulation to model deployment. It emphasizes hands-on learning through interactive exercises and code examples using popular libraries like Scikit-Learn, Keras, and TensorFlow.
Provides a comprehensive introduction to machine learning, covering both supervised and unsupervised learning algorithms. It includes theoretical foundations, practical examples, and discussion of real-world applications, offering a well-rounded understanding of machine learning concepts.
Classic reference for reinforcement learning, providing a comprehensive overview of the field. It covers both theoretical foundations and practical applications, offering insights into how reinforcement learning can be used to solve complex problems.
Offers a practical and accessible introduction to machine learning, with a focus on hands-on coding and real-world examples. It provides a quick start for learners who want to get up and running with machine learning quickly, offering a practical foundation for using AI Platform.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1v8ifo/ai
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Effort
1 hour
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Exposes learners to TensorFlow, a popular machine learning library, which is standard in the data science industry
Teaches learners how to deploy machine learning models to AI Platform for serving (prediction), a valuable skill in modern data science
Offers self-paced labs, a convenient and flexible learning format that accommodates diverse learner schedules
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