Managing Machine Learning Projects with Google Cloud
- 4.6
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Course Summary
This course teaches machine learning to business professionals, including how to identify business problems that can be solved with machine learning, how to select the right algorithms, and how to deploy models into a production environment.Key Learning Points
- Learn how to identify business problems that can be solved with machine learning
- Understand how to select the right algorithms for your business problem
- Learn how to deploy machine learning models into a production environment
Job Positions & Salaries of people who have taken this course might have
- Machine Learning Engineer
- USA: $112,000
- India: ₹1,100,000
- Spain: €45,000
- Data Scientist
- USA: $96,000
- India: ₹900,000
- Spain: €38,000
- Business Intelligence Analyst
- USA: $70,000
- India: ₹700,000
- Spain: €30,000
Related Topics for further study
- machine learning for business professionals
- identifying business problems
- selecting algorithms
- deploying models
- production environment
Learning Outcomes
- Learn how to identify business problems that can be solved with machine learning
- Understand how to select the right algorithms for your business problem
- Learn how to deploy machine learning models into a production environment
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics
- Basic programming skills, preferably in Python
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Data Science with Python
- Data Science Essentials
- Applied Machine Learning
Related Education Paths
- IBM Data Science Professional Certificate
- Google IT Automation with Python Professional Certificate
- AWS Certified Machine Learning - Specialty
Related Books
Description
Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.
Knowledge
- Assess the feasibility of your own ML use case and its ability to meaningfully impact your business.
- Identify the requirements to build, train, and evaluate an ML model.
- Define data characteristics and biases that affect the quality of ML models.
- Recognize key considerations for managing ML projects.
Outline
- Module 1: Introduction
- Introduction
- How to download course resources
- How to send feedback
- Course Slides
- Module 2: Identifying business value for using ML
- Introduction
- AI vs ML vs Deep Learning
- Phase 1: Assess feasibility
- Practice assessing the feasibility of ML use cases
- Worksheet
- Identifying business value for using ML
- Module 3: Defining ML as a practice
- Common ML problem types
- Standard algorithm and data
- Data quality
- Predictive insights and decisions
- More ML examples
- Practice series: Analyze the ML use case
- Saving the world's bees
- Google Assistant for accessibility
- Exercise review and Why ML now
- Module 3: Worksheet
- Defining ML as a practice
- Module 4: Building and evaluating ML models
- Features and labels
- Building labeled datasets
- Training an ML model
- General best practices
- Introduction to hands-on labs
- Lab 1: Review
- Building and evaluating ML models
- Module 5: Using ML responsibly and ethically
- Human bias in ML
- Google's AI Principles
- Common types of human bias
- Evaluating model fairness
- Guidelines and Hands-on Lab
- Lab 2: Review
- Using ML responsibly and ethically
- Module 6: Discovering ML use cases in day-to-day business
- Replacing rule-based systems with ML
- Automate processes and understand unstructured data
- Personalize applications with ML
- Creative uses of ML
- Sentiment analysis and Hands-on Lab
- Lab 3: Review
- Sentiment Analysis Worksheet
- Discovering ML use cases in day-to-day business
- Module 7: Managing ML projects successfully
- Key consideration 1: business value
- Data strategy (pillars 1–3)
- Data strategy (pillars 4–7)
- Data governance
- Build successful ML teams
- Create a culture of innovation and Hands-on Lab
- Lab 4: Review
- Managing ML projects successfully
- Module 8: Summary
- Summary
Summary of User Reviews
The Machine Learning for Business Professionals course on Coursera has received positive reviews from users. The course covers practical applications of machine learning in business and has been praised for its relevance and usefulness. Many users have found the course to be informative and engaging, making it a worthwhile investment for professionals looking to expand their skillset.Key Aspect Users Liked About This Course
The course has been praised for its practical focus on machine learning in business, offering users real-world examples and case studies to apply their learning.Pros from User Reviews
- Relevant and practical content
- Engaging and informative course structure
- Real-world examples and case studies provided
- High-quality video lectures and materials
- Good value for the investment
Cons from User Reviews
- Limited technical depth for advanced users
- Some users found the pace of the course to be slow
- Lack of hands-on exercises or assignments
- Course may not be suitable for those without prior knowledge of machine learning
- Some users found the course to be too basic
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