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Google Cloud Training
In this lab you train, evaluate, and deploy a machine learning model to predict a baby’s weight. You then send requests to the model to make online predictions. This lab is part of a series of labs on processing scientific data.
Register for this course and see more details by visiting:
OpenCourser.com/course/adhl3b/predict
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What's inside
Syllabus
Predict Baby Weight with TensorFlow on AI Platform
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners with an interest in either data science or machine learning
Introduces learners to industry-standard tools and techniques
Provides an opportunity for hands-on practice with machine learning models
May be too introductory for learners with prior experience in machine learning
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Save Predict Baby Weight with TensorFlow on AI Platform 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 Predict Baby Weight with TensorFlow on AI Platform with these
activities:
Review prerequisites: Algebra, Calculus
Show steps
Refresh your understanding of Algebra and Calculus to strengthen your foundation for this course.
Browse courses on
Algebra
Show steps
-
Study your Algebra and Calculus textbooks or notes.
-
Complete practice problems from textbooks or online resources.
-
Take practice quizzes or tests to assess your understanding.
Follow TensorFlow tutorials
Show steps
Deepen your understanding of TensorFlow by following guided tutorials to build your skills.
Browse courses on
TensorFlow
Show steps
-
Select tutorials aligned with the topics covered in this course.
-
Follow the tutorials step-by-step, implementing the code and experimenting with the examples.
-
Troubleshoot any errors or issues you encounter during the tutorials.
-
Reflect on what you learned and how it applies to this course's concepts.
Predict baby weight using a TensorFlow model
Show steps
Apply your knowledge by creating a TensorFlow model to predict baby weight.
Browse courses on
TensorFlow
Show steps
-
Gather and prepare the necessary data for training the model.
-
Build a TensorFlow model using the provided code as a starting point.
-
Train and evaluate the model to optimize its performance.
-
Deploy the model to make online predictions.
Two other activities
Expand to see all activities and additional details
Show all five activities
Build a baby weight prediction web application
Show steps
Consolidate your learning by building a web application that leverages your baby weight prediction model.
Browse courses on
Web Development
Show steps
-
Design the architecture and user interface for the application.
-
Develop the front-end and back-end components of the application.
-
Integrate the TensorFlow model into the application for prediction.
-
Deploy the application to a hosting platform.
-
Test and refine the application based on user feedback.
Kaggle: Predicting Baby Weight Competition
Show steps
Challenge yourself by participating in the Kaggle competition to showcase your baby weight prediction skills.
Browse courses on
Machine Learning
Show steps
-
Familiarize yourself with the competition rules and dataset.
-
Develop and refine your machine learning models.
-
Submit your predictions and track your progress on the leaderboard.
-
Analyze your results and learn from your mistakes.
-
Network with other participants and experts in the field.
Review prerequisites: Algebra, Calculus
Show steps
Refresh your understanding of Algebra and Calculus to strengthen your foundation for this course.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Study your Algebra and Calculus textbooks or notes.
- Complete practice problems from textbooks or online resources.
- Take practice quizzes or tests to assess your understanding.
Follow TensorFlow tutorials
Show steps
Deepen your understanding of TensorFlow by following guided tutorials to build your skills.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Select tutorials aligned with the topics covered in this course.
- Follow the tutorials step-by-step, implementing the code and experimenting with the examples.
- Troubleshoot any errors or issues you encounter during the tutorials.
- Reflect on what you learned and how it applies to this course's concepts.
Predict baby weight using a TensorFlow model
Show steps
Apply your knowledge by creating a TensorFlow model to predict baby weight.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Gather and prepare the necessary data for training the model.
- Build a TensorFlow model using the provided code as a starting point.
- Train and evaluate the model to optimize its performance.
- Deploy the model to make online predictions.
Two other activities
Expand to see all activities and additional details
Show all five activities
Build a baby weight prediction web application
Show steps
Consolidate your learning by building a web application that leverages your baby weight prediction model.
Browse courses on
Web Development
Show steps
Show steps
Show steps
- Design the architecture and user interface for the application.
- Develop the front-end and back-end components of the application.
- Integrate the TensorFlow model into the application for prediction.
- Deploy the application to a hosting platform.
- Test and refine the application based on user feedback.
Kaggle: Predicting Baby Weight Competition
Show steps
Challenge yourself by participating in the Kaggle competition to showcase your baby weight prediction skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Familiarize yourself with the competition rules and dataset.
- Develop and refine your machine learning models.
- Submit your predictions and track your progress on the leaderboard.
- Analyze your results and learn from your mistakes.
- Network with other participants and experts in the field.
Career center
Learners who complete Predict Baby Weight with TensorFlow on AI Platform will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use machine learning to solve real-world problems. This course can help you build a foundation in machine learning, which is essential for success in this role. The course covers topics such as data preprocessing, model training, and model evaluation. This knowledge will help you to develop and deploy machine learning models that can solve problems and create value for businesses.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course can help you build a foundation in data science, which is essential for success in this role. The course covers topics such as data wrangling, data analysis, and data visualization. This knowledge will help you to extract insights from data and make better decisions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help you build a foundation in software engineering, which is essential for success in this role. The course covers topics such as software design, software development, and software testing. This knowledge will help you to develop software systems that are reliable, efficient, and user-friendly.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. This course can help you build a foundation in data analysis, which is essential for success in this role. The course covers topics such as data collection, data cleaning, and data analysis. This knowledge will help you to identify insights from data and make better decisions.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. This course can help you build a foundation in business analysis, which is essential for success in this role. The course covers topics such as business process analysis, data analysis, and financial analysis. This knowledge will help you to identify and solve business problems.
Statistician
Statistician
Statisticians use data to solve problems. This course can help you build a foundation in statistics, which is essential for success in this role. The course covers topics such as probability, inference, and regression. This knowledge will help you to make informed decisions based on data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical models to solve business problems. This course can help you build a foundation in operations research, which is essential for success in this role. The course covers topics such as linear programming, integer programming, and network optimization. This knowledge will help you to develop solutions to complex business problems.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. This course can help you build a foundation in financial analysis, which is essential for success in this role. The course covers topics such as financial statement analysis, valuation, and portfolio management. This knowledge will help you to make informed investment decisions.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. This course can help you build a foundation in market research, which is essential for success in this role. The course covers topics such as survey design, data analysis, and market segmentation. This knowledge will help you to understand consumer needs and develop marketing strategies.
Product Manager
Product Manager
Product Managers use data to develop and launch new products. This course can help you build a foundation in product management, which is essential for success in this role. The course covers topics such as product design, product development, and product marketing. This knowledge will help you to develop and launch successful products.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. This course can help you build a foundation in quantitative analysis, which is essential for success in this role. The course covers topics such as financial modeling, statistical analysis, and risk management. This knowledge will help you to make informed investment decisions.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and mitigate risks. This course can help you build a foundation in risk analysis, which is essential for success in this role. The course covers topics such as risk identification, risk assessment, and risk management. This knowledge will help you to identify and mitigate risks.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to make business decisions. This course can help you build a foundation in business intelligence, which is essential for success in this role. The course covers topics such as data warehousing, data mining, and data visualization. This knowledge will help you to make informed business decisions.
Health Data Analyst
Health Data Analyst
Health Data Analysts use data to improve healthcare outcomes. This course can help you build a foundation in health data analysis, which is essential for success in this role. The course covers topics such as medical data analysis, statistical analysis, and machine learning. This knowledge will help you to identify and mitigate risks.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines. This course can help you build a foundation in data engineering, which is essential for success in this role. The course covers topics such as data integration, data warehousing, and data processing. This knowledge will help you to build data pipelines that are reliable, efficient, and scalable.
For more career information including salaries, visit:
OpenCourser.com/course/adhl3b/predict
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
Predict Baby Weight with TensorFlow on AI Platform.
Save
Comprehensive guide to deep learning and provides a deep understanding of the underlying concepts. It covers a wide range of topics including convolutional neural networks, recurrent neural networks, and deep reinforcement learning.
Practical guide to machine learning with Scikit-Learn, Keras, and TensorFlow and valuable resource for both beginners and experienced practitioners. It covers a wide range of topics including data preprocessing, model building, and training.
Save
Provides a comprehensive overview of machine learning from a Bayesian and optimization perspective. It covers a wide range of topics including supervised and unsupervised learning, model selection, and overfitting.
Provides a comprehensive overview of machine learning from an algorithmic perspective. It covers a wide range of topics including supervised and unsupervised learning, model selection, and overfitting.
Save
Provides a comprehensive overview of machine learning from an algorithmic perspective. It covers a wide range of topics including supervised and unsupervised learning, model selection, and overfitting.
Provides a comprehensive overview of machine learning and valuable resource for both beginners and experienced practitioners. It covers a wide range of topics including supervised and unsupervised learning, model selection, and overfitting.
Comprehensive guide to machine learning for beginners and provides a gentle introduction to the underlying concepts. It covers a wide range of topics including supervised and unsupervised learning, model selection, and overfitting.
Provides a practical guide to machine learning and valuable resource for both beginners and experienced practitioners. It covers a wide range of topics including data preprocessing, model building, and training.
Save
Comprehensive guide to machine learning and valuable resource for both beginners and experienced practitioners. It covers a wide range of topics including supervised and unsupervised learning, model selection, and overfitting.
Comprehensive guide to machine learning for beginners and provides a gentle introduction to the underlying concepts. It covers a wide range of topics including supervised and unsupervised learning, model selection, and overfitting.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/adhl3b/predict
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Effort
1 hour 30 minutes
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Suitable for learners with an interest in either data science or machine learning
Introduces learners to industry-standard tools and techniques
Provides an opportunity for hands-on practice with machine learning models
May be too introductory for learners with prior experience in machine learning
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