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Google Cloud
This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases.
This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
Register for this course and see more details by visiting:
OpenCourser.com/course/xeqn3a/machine
What's inside
Syllabus
Introduction
Understanding the ML Enterprise Workflow
Data in the Enterprise
Science of Machine Learning and Custom Training
Read more
Syllabus
Introduction
Understanding the ML Enterprise Workflow
Data in the Enterprise
Science of Machine Learning and Custom Training
Read more
Vertex Vizier Hyperparameter Tuning
Prediction and Model Monitoring Using Vertex AI
Vertex AI Pipelines
Best Practices for ML Development
Course Summary
Series Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides new and unique perspectives on the machine learning lifecycle, offering a rare viewpoint in the industry
Designed for professionals in the machine learning field, particularly those seeking to enhance their expertise in managing the machine learning workflow
Save this course
Save Machine Learning in the Enterprise 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 Machine Learning in the Enterprise with these
activities:
Review the fundamentals of ML
Show steps
Refreshes fundamental concepts in ML to help bridge any foundational gaps before taking the course.
Browse courses on
Machine Learning
Show steps
-
Review linear regression and logistic regression
-
Understand the concept of overfitting and underfitting
Explore ML case studies on Kaggle
Show steps
Provides hands-on experience with ML problem-solving.
Browse courses on
Kaggle Competitions
Show steps
-
Find a relevant ML case study on Kaggle
-
Follow the tutorial to understand the ML workflow
-
Experiment with different ML models and parameters
Participate in online discussion forums
Show steps
Engages students with peers, fostering collaboration and knowledge exchange.
Show steps
-
Join online discussion forums related to ML
-
Ask questions and share insights
One other activity
Expand to see all activities and additional details
Show all four activities
Build an ML model for a real-world problem
Show steps
Applies ML concepts to solve a specific problem and reinforces the ML workflow.
Browse courses on
Capstone Project
Show steps
-
Define the problem and gather data
-
Explore and preprocess the data
-
Train and evaluate different ML models
-
Deploy the best model and monitor its performance
Review the fundamentals of ML
Show steps
Refreshes fundamental concepts in ML to help bridge any foundational gaps before taking the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review linear regression and logistic regression
- Understand the concept of overfitting and underfitting
Explore ML case studies on Kaggle
Show steps
Provides hands-on experience with ML problem-solving.
Browse courses on
Kaggle Competitions
Show steps
Show steps
Show steps
- Find a relevant ML case study on Kaggle
- Follow the tutorial to understand the ML workflow
- Experiment with different ML models and parameters
Participate in online discussion forums
Show steps
Engages students with peers, fostering collaboration and knowledge exchange.
Show steps
Show steps
Show steps
- Join online discussion forums related to ML
- Ask questions and share insights
One other activity
Expand to see all activities and additional details
Show all four activities
Build an ML model for a real-world problem
Show steps
Applies ML concepts to solve a specific problem and reinforces the ML workflow.
Browse courses on
Capstone Project
Show steps
Show steps
Show steps
- Define the problem and gather data
- Explore and preprocess the data
- Train and evaluate different ML models
- Deploy the best model and monitor its performance
Career center
Learners who complete Machine Learning in the Enterprise will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, organize, interpret, and present data to help businesses understand their market, customers, and operations. Machine Learning in the Enterprise may be useful for learning how to prepare and manage data for use in machine learning, as well as best practices for developing and deploying machine learning applications.
Data Scientist
Data Scientist
A Data Scientist combines machine learning, statistics, data analysis, and algorithms to solve business problems. The Machine Learning in the Enterprise course may be useful for learning about the workflow and processes enterprises use regarding machine learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop, deploy, and maintain machine learning applications. The Machine Learning in the Enterprise course may be useful for learning about the workflow and processes enterprises use regarding machine learning.
Business Analyst
Business Analyst
Business Analysts solve business problems by applying analytical and problem-solving skills to understand requirements, processes, and technology. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to solve business problems.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software applications. The Machine Learning in the Enterprise course may be useful for learning how to apply machine learning to software applications.
Financial Analyst
Financial Analyst
Financial Analysts make recommendations on investments and financial planning. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to analyze financial data and make more informed decisions.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to improve products and services.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to improve operational efficiency and productivity.
Consultant
Consultant
Consultants provide advice and expertise to organizations on a variety of topics. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to solve business problems and improve decision-making.
Technical Writer
Technical Writer
Technical Writers create documentation and other materials to explain technical information to a non-technical audience. The Machine Learning in the Enterprise course may be useful for learning how machine learning works and how to explain it to others.
Customer Success Manager
Customer Success Manager
Customer Success Managers help customers achieve their desired outcomes with a product or service. The Machine Learning in the Enterprise course may be useful for learning about the benefits of machine learning and how it can be used to improve customer satisfaction and retention.
Teacher
Teacher
Teachers educate students at all levels, from elementary school to college. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to improve teaching and learning.
Researcher
Researcher
Researchers conduct scientific research to develop new knowledge and technologies. The Machine Learning in the Enterprise course may be useful for learning about the latest advances in machine learning and how they can be applied to a variety of fields.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing plans to promote products and services. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to target marketing campaigns and improve customer engagement.
Sales Engineer
Sales Engineer
Sales Engineers help customers implement and use technology solutions. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to improve business outcomes and solve customer problems.
For more career information including salaries, visit:
OpenCourser.com/course/xeqn3a/machine
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
Machine Learning in the Enterprise.
Provides a comprehensive overview of machine learning algorithms and techniques. It uses popular Python libraries such as Scikit-Learn, Keras, and TensorFlow.
Provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as statistical pattern recognition, neural networks, and support vector machines.
Provides a comprehensive overview of data mining for business intelligence. It covers topics such as data preparation, data mining algorithms, and data visualization.
Provides a practical introduction to deep learning using Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a probabilistic perspective on machine learning. It covers topics such as Bayesian inference, Gaussian processes, and Markov chain Monte Carlo.
Provides a practical introduction to machine learning using R. It covers topics such as data preparation, model selection, and model evaluation.
Introduces deep learning concepts and techniques. It uses the Fastai library, which makes deep learning accessible to coders without a PhD.
Provides a comprehensive introduction to reinforcement learning. It covers topics such as Markov decision processes, value functions, and policy gradients.
Provides a practical introduction to machine learning. It covers topics such as data preparation, model selection, and model evaluation.
Provides a practical introduction to data science for business managers. It covers topics such as data collection, data analysis, and data visualization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xeqn3a/machine
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Time
128 hours
Level
Introductory
Via
Pluralsight
Instructor
Google Cloud
Language
English
Good to know
Provides new and unique perspectives on the machine learning lifecycle, offering a rare viewpoint in the industry
Designed for professionals in the machine learning field, particularly those seeking to enhance their expertise in managing the machine learning workflow
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