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Business professionals in non-technical roles have a unique opportunity to lead/influence machine learning projects. If you have questions about machine learning & want to understand how to use it, this course is for you.
Read more
Business professionals in non-technical roles have a unique opportunity to lead/influence machine learning projects. If you have questions about machine learning & want to understand how to use it, this course is for you.
Read more
Business professionals in non-technical roles have a unique opportunity to lead/influence machine learning projects. If you have questions about machine learning & want to understand how to use it, this course is for you.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/xp2ug3/managing
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What's inside
Syllabus
Introduction
Identifying business value for using ML
Defining ML as a practice
Building and evaluating ML models
Read more
Syllabus
Introduction
Identifying business value for using ML
Defining ML as a practice
Building and evaluating ML models
Read more
Using ML responsibly and ethically
Discovering ML use cases in day-to-day business
Managing ML projects successfully
Summary
Course Resources
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Designed for learners seeking fundamental knowledge to make informed decisions in machine learning projects
Taught by Google Cloud, a reputable organization in the field
Suitable for learners with no prior technical background in machine learning
Provides practical guidance on identifying and evaluating machine learning use cases
Helps learners understand the ethical and responsible implications of machine learning
Covers the fundamentals of machine learning model building and evaluation
Save this course
Save Managing Machine Learning Projects with Google Cloud 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 Managing Machine Learning Projects with Google Cloud with these
activities:
Read 'Machine Learning for Business Professionals'
Show steps
Gain a comprehensive overview of machine learning concepts, applications, and business implications from a reputable source.
View
Data Science
on Amazon
Show steps
-
Read the book thoroughly, taking notes
-
Highlight key concepts and examples
Review high school level math
Show steps
Brush up on basic algebra, functions, and logarithms to strengthen foundational knowledge for understanding machine learning algorithms.
Browse courses on
Linear Functions
Show steps
-
Review notes from algebra classes
-
Retake practice problems
-
Complete online practice exercises
Work through practice problems on machine learning concepts
Show steps
Solve a variety of practice problems to reinforce understanding of different machine learning algorithms and techniques.
Browse courses on
Supervised Learning
Show steps
-
Find practice problems online
-
Work through problems step-by-step
-
Review solutions and identify areas for improvement
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a visual representation of a machine learning model
Show steps
Create a visual representation of a machine learning model, such as a diagram or flowchart, to enhance understanding of model structure and functioning.
Browse courses on
Decision Trees
Show steps
-
Choose a simple machine learning model
-
Identify key components and relationships
-
Create a visual representation using tools like draw.io or Lucidchart
Follow online tutorials on specific machine learning applications
Show steps
Enhance practical knowledge by following online tutorials that demonstrate real-world applications of machine learning in various fields.
Browse courses on
Natural Language Processing
Show steps
-
Identify specific machine learning applications of interest
-
Search for reputable online tutorials
-
Follow tutorials step-by-step, implementing the techniques
Design a presentation on a machine learning project idea
Show steps
Develop a well-structured presentation that outlines a machine learning project idea, including problem statement, proposed approach, and potential impact.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a business problem suitable for machine learning
-
Research and propose a machine learning solution
-
Create a presentation that includes project goals, methodology, and expected outcomes
Engage in peer discussions on machine learning concepts
Show steps
Collaborate with peers to discuss and clarify machine learning concepts, exchange ideas, and enhance understanding through shared perspectives.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Identify a group of peers with similar interests
-
Establish regular discussion sessions
-
Take turns presenting and facilitating discussions on specific topics
Read 'Machine Learning for Business Professionals'
Show steps
Gain a comprehensive overview of machine learning concepts, applications, and business implications from a reputable source.
View
Data Science
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly, taking notes
- Highlight key concepts and examples
Review high school level math
Show steps
Brush up on basic algebra, functions, and logarithms to strengthen foundational knowledge for understanding machine learning algorithms.
Browse courses on
Linear Functions
Show steps
Show steps
Show steps
- Review notes from algebra classes
- Retake practice problems
- Complete online practice exercises
Work through practice problems on machine learning concepts
Show steps
Solve a variety of practice problems to reinforce understanding of different machine learning algorithms and techniques.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Find practice problems online
- Work through problems step-by-step
- Review solutions and identify areas for improvement
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a visual representation of a machine learning model
Show steps
Create a visual representation of a machine learning model, such as a diagram or flowchart, to enhance understanding of model structure and functioning.
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Choose a simple machine learning model
- Identify key components and relationships
- Create a visual representation using tools like draw.io or Lucidchart
Follow online tutorials on specific machine learning applications
Show steps
Enhance practical knowledge by following online tutorials that demonstrate real-world applications of machine learning in various fields.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify specific machine learning applications of interest
- Search for reputable online tutorials
- Follow tutorials step-by-step, implementing the techniques
Design a presentation on a machine learning project idea
Show steps
Develop a well-structured presentation that outlines a machine learning project idea, including problem statement, proposed approach, and potential impact.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a business problem suitable for machine learning
- Research and propose a machine learning solution
- Create a presentation that includes project goals, methodology, and expected outcomes
Engage in peer discussions on machine learning concepts
Show steps
Collaborate with peers to discuss and clarify machine learning concepts, exchange ideas, and enhance understanding through shared perspectives.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Identify a group of peers with similar interests
- Establish regular discussion sessions
- Take turns presenting and facilitating discussions on specific topics
Career center
Learners who complete Managing Machine Learning Projects with Google Cloud will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists develop, build, and deploy machine learning models to solve business problems. This course provides a solid foundation in the principles and practices of machine learning, which is essential for success in this role. You will learn how to identify business value for using ML, define ML as a practice, build and evaluate ML models, and use ML responsibly and ethically. This course will also help you discover ML use cases in day-to-day business and manage ML projects successfully.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models to solve business problems. This course provides a solid foundation in the principles and practices of machine learning, which is essential for success in this role. You will learn how to identify business value for using ML, define ML as a practice, build and evaluate ML models, and use ML responsibly and ethically. This course will also help you discover ML use cases in day-to-day business and manage ML projects successfully.
Business Analyst
Business Analyst
Business Analysts help businesses improve their performance by identifying and solving problems. This course will help Business Analysts understand the potential of machine learning and how it can be used to solve business problems. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course will help Product Managers understand the potential of machine learning and how it can be used to create new products and features. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make better decisions. This course will help Data Analysts understand the potential of machine learning and how it can be used to analyze data and make predictions. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Chief Data Officer
Chief Data Officer
Chief Data Officers are responsible for managing the data assets of an organization. This course will help Chief Data Officers understand the potential of machine learning and how it can be used to extract value from data. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and implement AI solutions. This course will help Artificial Intelligence Engineers understand the potential of machine learning and how it can be used to create AI applications. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Chief Technology Officer
Chief Technology Officer
Chief Technology Officers are responsible for leading the technology strategy of an organization. This course will help Chief Technology Officers understand the potential of machine learning and how it can be used to transform businesses. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Data Warehouse Manager
Data Warehouse Manager
Data Warehouse Managers are responsible for managing data warehouses. This course will help Data Warehouse Managers understand the potential of machine learning and how it can be used to improve data warehouse performance. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. This course will help Marketing Managers understand the potential of machine learning and how it can be used to target customers and track the effectiveness of marketing campaigns. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Chief Analytics Officer
Chief Analytics Officer
Chief Analytics Officers are responsible for leading the analytics strategy of an organization. This course will help Chief Analytics Officers understand the potential of machine learning and how it can be used to drive business decisions. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. This course will help Project Managers understand the potential of machine learning and how it can be used to improve project management processes. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams to achieve sales goals. This course will help Sales Managers understand the potential of machine learning and how it can be used to generate leads, close deals, and improve customer satisfaction. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Software Engineer
Software Engineer
Software Engineers design, develop, and implement software applications. This course will help Software Engineers understand the potential of machine learning and how it can be used to create software applications. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
Consultant
Consultant
Consultants help businesses improve their performance by providing expert advice and solutions. This course will help Consultants understand the potential of machine learning and how it can be used to solve business problems. You will learn how to identify business value for using ML, define ML as a practice, and discover ML use cases in day-to-day business. This course will also help you understand the phases of an ML project and the considerations within each.
For more career information including salaries, visit:
OpenCourser.com/course/xp2ug3/managing
Reading list
We've selected 16 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
Managing Machine Learning Projects with Google Cloud.
Comprehensive introduction to reinforcement learning. It covers the basics of reinforcement learning, how to use reinforcement learning to solve problems, and how to design reinforcement learning algorithms.
Comprehensive guide to building intelligent systems using machine learning. It covers the basics of machine learning, how to use Scikit-Learn and TensorFlow to build machine learning models, and how to deploy machine learning models.
Practical introduction to natural language processing using Python. It covers the basics of natural language processing, how to use Python libraries to process natural language, and how to build natural language processing applications.
Practical introduction to time series analysis and forecasting. It covers the basics of time series analysis, how to use Python libraries to analyze and forecast time series, and how to build time series forecasting applications.
Practical introduction to machine learning. It covers the basics of machine learning, how to use Python libraries to build machine learning models, and how to deploy machine learning models.
Practical introduction to machine learning using R. It covers the basics of machine learning, how to use R libraries to build machine learning models, and how to deploy machine learning models.
Practical introduction to machine learning from scratch. It covers the basics of machine learning, how to build machine learning models from scratch, and how to deploy machine learning models.
Practical guide to getting started with machine learning using Python. It covers the basics of machine learning, how to use Python libraries to build machine learning models, and how to deploy machine learning models.
Practical guide to getting started with deep learning using Python. It covers the basics of deep learning, how to use Python libraries to build deep learning models, and how to deploy deep learning models.
Great resource for learners who want to get a quick overview of machine learning. It covers the basics in a simple and easy-to-understand way.
Save
Comprehensive textbook on deep learning in Chinese. It covers the fundamentals, algorithms, and applications of deep learning, and valuable resource for learners who want to understand the field in more depth.
Save
Comprehensive textbook on machine learning in Chinese. It covers the fundamentals, algorithms, and applications of machine learning, and valuable resource for learners who want to understand the field in more depth.
Is an excellent resource for learners who want to get started with machine learning in Python. It covers the basics of machine learning, as well as how to use Python libraries for machine learning.
Comprehensive guide to machine learning with R. It covers the basics of machine learning, as well as how to use R libraries for machine learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for learners who want to understand the theoretical foundations of machine learning.
Practical guide to machine learning for hackers. It covers the basics of machine learning, as well as how to use machine learning to solve real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xp2ug3/managing
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Time
262 hours
Level
Introductory
Via
Pluralsight
Instructor
Google Cloud
Language
English
Good to know
Designed for learners seeking fundamental knowledge to make informed decisions in machine learning projects
Taught by Google Cloud, a reputable organization in the field
Suitable for learners with no prior technical background in machine learning
Provides practical guidance on identifying and evaluating machine learning use cases
Helps learners understand the ethical and responsible implications of machine learning
Covers the fundamentals of machine learning model building and evaluation
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