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Mírian Silva
In this 1-hour long project-based course, you will be able to create, evaluate and save a machine learning model (without writing a single line of code) using Watson Studio on IBM Cloud Platform, and you will make deployment of the model and try out as a web...
Read more
In this 1-hour long project-based course, you will be able to create, evaluate and save a machine learning model (without writing a single line of code) using Watson Studio on IBM Cloud Platform, and you will make deployment of the model and try out as a web...
Read more
In this 1-hour long project-based course, you will be able to create, evaluate and save a machine learning model (without writing a single line of code) using Watson Studio on IBM Cloud Platform, and you will make deployment of the model and try out as a web service frontend to make predictions.
This guided project is for Data Scientists, Machine Learning Engineers, and Developers who want a way to deliver their machine learning code available to be integrated into an application and using it as a web service. We will do everything in a development mode without any costs using a free IBM Cloud account.
To be successful in this project, you should be familiar with machine learning methodologies, like training, prediction, evaluation, and basic knowledge in some machine learning algorithms is appreciated too, so that way you will understand the results before making a deployment.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/3a09sz/deploy
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Provides a comprehensive understanding of the machine learning development and deployment processes
Suitable for Data Scientists, Machine Learning Engineers, and Developers aiming to integrate machine learning models into applications
Emphasizes practical application through model creation, evaluation, and deployment
Requires minimal technical expertise, as no coding is involved in model development
Facilitates quick deployment of models as web services, enabling learners to share their work
Benefits professionals seeking to enhance their skills in machine learning model integration and deployment
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Reviews summary
Introductory course with confusing delivery
This course received a 1-star review, so the sentiment of the reviews is negative. The reviewer emphasizes difficulty understanding the instructor's accent and states that there were no updates to outdated course components.
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 Deploy a predictive machine learning model using IBM Cloud with these
activities:
Review 'Machine Learning with Python'
Show steps
Review a comprehensive book on machine learning to supplement course material and gain a broader perspective on the field.
View
Machine Learning with PyTorch and Scikit-Learn:...
on Amazon
Show steps
-
Read selected chapters or sections
-
Take notes and summarize key concepts
Review basic machine learning algorithms
Show steps
Review basic machine learning algorithms to build strong foundations and enhance understanding of the course material.
Browse courses on
Machine Learning Algorithms
Show steps
-
Go over basic supervised and unsupervised learning algorithms
-
Review the concepts of linear regression, logistic regression, decision trees, and clustering
Solve machine learning practice problems
Show steps
Engage in practice problems to solidify understanding of machine learning concepts and techniques covered in the course.
Show steps
-
Review solutions and identify areas for improvement
-
Find practice problems online or in textbooks
-
Attempt to solve the problems independently
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow guided tutorials on advanced machine learning techniques
Show steps
Explore advanced machine learning techniques through guided tutorials to expand knowledge and skills beyond the course scope.
Show steps
-
Identify specific advanced machine learning techniques of interest
-
Find reputable online tutorials or courses
-
Follow the tutorials and complete the exercises
Create a machine learning project using Watson Studio
Show steps
Develop a practical project using Watson Studio to apply machine learning skills, enhance problem-solving abilities, and build a portfolio.
Show steps
-
Identify a problem or dataset that aligns with interests
-
Design and implement a machine learning solution using Watson Studio
-
Document the project and its results
Participate in machine learning competitions
Show steps
Engage in machine learning competitions to test skills, learn from others, and stay updated with industry trends.
Show steps
-
Find suitable machine learning competitions online
-
Form a team or participate individually
-
Develop and submit solutions
Develop a long-term machine learning project
Show steps
Embark on an ambitious machine learning project to challenge oneself, gain valuable experience, and create something meaningful.
Show steps
-
Brainstorm and define a project scope
-
Gather and prepare data
-
Develop and refine machine learning models
-
Evaluate and deploy the project
Review 'Machine Learning with Python'
Show steps
Review a comprehensive book on machine learning to supplement course material and gain a broader perspective on the field.
View
Machine Learning with PyTorch and Scikit-Learn:...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters or sections
- Take notes and summarize key concepts
Review basic machine learning algorithms
Show steps
Review basic machine learning algorithms to build strong foundations and enhance understanding of the course material.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Go over basic supervised and unsupervised learning algorithms
- Review the concepts of linear regression, logistic regression, decision trees, and clustering
Solve machine learning practice problems
Show steps
Engage in practice problems to solidify understanding of machine learning concepts and techniques covered in the course.
Show steps
Show steps
Show steps
- Review solutions and identify areas for improvement
- Find practice problems online or in textbooks
- Attempt to solve the problems independently
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow guided tutorials on advanced machine learning techniques
Show steps
Explore advanced machine learning techniques through guided tutorials to expand knowledge and skills beyond the course scope.
Show steps
Show steps
Show steps
- Identify specific advanced machine learning techniques of interest
- Find reputable online tutorials or courses
- Follow the tutorials and complete the exercises
Create a machine learning project using Watson Studio
Show steps
Develop a practical project using Watson Studio to apply machine learning skills, enhance problem-solving abilities, and build a portfolio.
Show steps
Show steps
Show steps
- Identify a problem or dataset that aligns with interests
- Design and implement a machine learning solution using Watson Studio
- Document the project and its results
Participate in machine learning competitions
Show steps
Engage in machine learning competitions to test skills, learn from others, and stay updated with industry trends.
Show steps
Show steps
Show steps
- Find suitable machine learning competitions online
- Form a team or participate individually
- Develop and submit solutions
Develop a long-term machine learning project
Show steps
Embark on an ambitious machine learning project to challenge oneself, gain valuable experience, and create something meaningful.
Show steps
Show steps
Show steps
- Brainstorm and define a project scope
- Gather and prepare data
- Develop and refine machine learning models
- Evaluate and deploy the project
Career center
Learners who complete Deploy a predictive machine learning model using IBM Cloud will develop knowledge and skills
that may be useful to these careers:
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course provides a solid foundation in machine learning, which is a key technology for personalizing marketing campaigns and improving customer engagement. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Sales Manager
Sales Manager
Sales Managers are responsible for leading sales teams and achieving sales targets. This course provides a solid foundation in machine learning, which is a key technology for automating sales processes and improving customer relationships. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course provides a solid foundation in machine learning, which is a key skill for Data Scientists. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, building, and maintaining machine learning models. This course provides hands-on experience with the entire machine learning pipeline, from data preparation to model deployment. By completing this course, you will be well-prepared for a career in this field.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. This course provides a solid foundation in machine learning, which is a key technology for automating business processes and improving decision-making. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. This course provides a solid foundation in machine learning, which is a key technology for many modern software applications. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course provides a solid foundation in machine learning, which is a key skill for Data Analysts. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making investment recommendations. This course provides a solid foundation in machine learning, which is a key technology for automating financial analysis and improving investment performance. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Product Manager
Product Manager
Product Managers are responsible for planning, developing, and launching new products. This course provides a solid foundation in machine learning, which is a key technology for developing innovative new products. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Operations Manager
Operations Manager
Operations Managers are responsible for planning and executing day-to-day operations. This course provides a solid foundation in machine learning, which is a key technology for automating operations processes and improving efficiency. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying and mitigating risks. This course provides a solid foundation in machine learning, which is a key technology for automating risk assessment and improving risk management. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Compliance Officer
Compliance Officer
Compliance Officers are responsible for ensuring that organizations comply with laws and regulations. This course provides a solid foundation in machine learning, which is a key technology for automating compliance processes and improving compliance management. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Data Protection Officer
Data Protection Officer
Data Protection Officers are responsible for protecting personal data and ensuring compliance with data protection laws. This course provides a solid foundation in machine learning, which is a key technology for automating data protection processes and improving data protection management. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Cybersecurity Analyst
Cybersecurity Analyst
Cybersecurity Analysts are responsible for protecting organizations from cyberattacks. This course provides a solid foundation in machine learning, which is a key technology for automating cybersecurity processes and improving cybersecurity management. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
Fraud Analyst
Fraud Analyst
Fraud Analysts are responsible for detecting and preventing fraud. This course provides a solid foundation in machine learning, which is a key technology for automating fraud detection and improving fraud prevention. By learning how to create, evaluate, and deploy machine learning models, you will be well-prepared for a career in this field.
For more career information including salaries, visit:
OpenCourser.com/course/3a09sz/deploy
Reading list
We've selected 14 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
Deploy a predictive machine learning model using IBM Cloud.
Provides a comprehensive introduction to statistical learning. It covers a wide range of statistical learning concepts and techniques, and is particularly useful for those who want to gain a deeper understanding of the underlying principles of statistical learning.
Covers a wide range of machine learning concepts, including supervised and unsupervised learning, feature engineering, and model evaluation. It comprehensive guide to machine learning with Python and is particularly useful for those who want to gain a deeper understanding of the underlying principles of machine learning.
Provides a comprehensive introduction to machine learning from a Bayesian and optimization perspective. It covers a wide range of machine learning concepts and techniques, and is particularly useful for those who want to gain a deeper understanding of the underlying principles of machine learning.
Provides a comprehensive introduction to pattern recognition and machine learning. It covers a wide range of pattern recognition and machine learning concepts and techniques, and is particularly useful for those who want to gain a deeper understanding of the underlying principles of pattern recognition and machine learning.
Provides a comprehensive introduction to reinforcement learning. It covers a wide range of reinforcement learning concepts and techniques, and is particularly useful for those who want to gain a deeper understanding of the underlying principles of reinforcement learning.
Provides a practical introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of machine learning algorithms and techniques, and is particularly useful for those who want to gain hands-on experience with machine learning.
Provides a comprehensive introduction to deep reinforcement learning. It covers a wide range of deep reinforcement learning concepts and techniques, and is particularly useful for those who want to gain a deeper understanding of the underlying principles of deep reinforcement learning.
Provides a comprehensive introduction to deep learning with Python. It covers a wide range of deep learning concepts and techniques, and is particularly useful for those who want to gain a deeper understanding of the underlying principles of deep learning.
Provides a practical introduction to machine learning with TensorFlow. It covers a wide range of machine learning concepts and techniques, and is particularly useful for those who want to gain hands-on experience with machine learning.
Provides a practical introduction to machine learning for hackers. It covers a wide range of machine learning concepts and techniques, and is particularly useful for those who want to gain hands-on experience with machine learning.
Save
Provides a practical introduction to machine learning for business. It covers a wide range of machine learning concepts and techniques, and is particularly useful for those who want to gain hands-on experience with machine learning.
Provides a practical introduction to machine learning with R. It covers a wide range of machine learning concepts and techniques, and is particularly useful for those who want to gain hands-on experience with machine learning.
Provides a practical introduction to machine learning with Python. It covers a wide range of machine learning concepts and techniques, and is particularly useful for those who want to gain hands-on experience with machine learning.
Provides a practical introduction to machine learning. It covers a wide range of machine learning concepts and techniques, and is particularly useful for those who want to gain hands-on experience with machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3a09sz/deploy
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Mírian Silva
Good to know
Provides a comprehensive understanding of the machine learning development and deployment processes
Suitable for Data Scientists, Machine Learning Engineers, and Developers aiming to integrate machine learning models into applications
Emphasizes practical application through model creation, evaluation, and deployment
Requires minimal technical expertise, as no coding is involved in model development
Facilitates quick deployment of models as web services, enabling learners to share their work
Benefits professionals seeking to enhance their skills in machine learning model integration and deployment
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