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Authored by Google Cloud
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Machine Learning
Production Environments
ML System Design
ML Deployment
Cloud Computing
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Taught by Google Cloud experts, recognized for their innovation in ML
Suitable for professionals wanting to build a high performing machine learning system for production
Delves into best practices for ML systems in production environments
Requires prior knowledge in ML and experience in production environments
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Activities
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Production Machine Learning Systems.
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Career center
Learners who complete Production Machine Learning Systems will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, maintain, and develop solutions that leverage machine learning. This course may be useful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
Data Scientist
Data Scientist
Data Scientists explore, analyze, and interpret data to extract insights and trends. This course may be useful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
Software Engineer
Software Engineer
A Software Engineer who focuses on deploying ML models may find this course useful in understanding the components of a high-performing ML system that can be deployed to production environments.
Data Analyst
Data Analyst
Data Analysts explore, analyze, and interpret data to extract insights and trends. This course may be useful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers' responsibilities include designing, building, and testing ML models. This course may be helpful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
Data Engineer
Data Engineer
Data Engineers are responsible for designing and maintaining the systems and processes that manage data. This course may be useful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
Business Analyst
Business Analyst
Business Analysts apply advanced knowledge of business processes and strategies to solve problems within an organization. This course may be helpful in understanding the components of a high-performing ML system that can be deployed and optimized in production environments.
Product Manager
Product Manager
Product Managers are responsible for the development and introduction of new products, from initial concept all the way through to launch. This course may be useful in understanding the components of a high-performing ML system that will be deployed to production environments.
Systems Analyst
Systems Analyst
Systems Analysts apply their knowledge of systems and procedures to improving how organizations harness technology and human resources. This course may be useful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
Statistician
Statistician
Statisticians apply their knowledge of statistical methods and techniques to collect, analyze, and interpret data. This course may be useful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts apply advanced analytical methods to improve decision-making and problem-solving within organizations. This course may be useful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to solve business problems. This course may be useful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty, particularly in the insurance and finance industries. This course may be useful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and economics to advise clients on investment opportunities. This course may be useful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks within organizations. This course may be useful in understanding the components of a high-performing ML system that can be deployed to production environments at scale.
For more career information including salaries, visit:
OpenCourser.com/course/u3thsq/production
Reading list
We haven't picked any books for this reading list yet.
Gentle introduction to machine learning, suitable for those with no prior knowledge of the field, with a focus on understanding the concepts rather than the math.
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Provides a comprehensive treatment of machine learning from a probabilistic perspective, covering a wide range of topics from Bayesian inference to deep learning.
Practical guide to machine learning for programmers, with a focus on using Python to build and deploy machine learning models.
Provides a balanced treatment of both statistical and machine learning methods, making it accessible to a wide audience.
Practical guide to machine learning with Python, covering a wide range of topics from data preprocessing to model evaluation.
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Comprehensive and authoritative reference on deep learning, covering a wide range of topics from neural networks to reinforcement learning.
Practical guide to machine learning for those with no prior experience, covering a wide range of topics from data preprocessing to model evaluation. It great hands-on tutorial to pick up skills in machine learning.
While not focused specifically on Machine learning, this book covers a broad range of topics in Artificial Intelligence including machine learning, and good companion to delve deeper into the theoretical and technical aspects of the field.
Offers a concise yet comprehensive introduction to machine learning, covering essential concepts and algorithms in just over 100 pages. It balances theory and practice, making it suitable for data professionals looking to expand their knowledge or prepare for interviews. It includes illustrations, models, and algorithms with Python examples. This book is excellent for gaining a broad understanding and serves as a valuable quick reference.
A highly practical book that guides readers through building intelligent systems using popular Python libraries. It starts with fundamental techniques like linear regression and progresses to deep neural networks. is ideal for those who prefer a hands-on approach with code examples and exercises. It is widely used as a textbook and reference for practitioners.
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Considered a foundational text in the field of deep learning, this book provides a comprehensive theoretical and conceptual understanding of neural networks and deep learning techniques. It covers essential mathematical prerequisites like linear algebra and probability. While theoretically oriented, it crucial resource for those wanting to delve deeply into the mechanics of deep learning and is often used in graduate-level courses.
Provides an accessible introduction to statistical learning methods, which form the basis of many machine learning algorithms. It focuses on concepts and applications rather than rigorous mathematical proofs, making it suitable for a broad audience with a statistics background. It is often used as a textbook for undergraduate and graduate courses and offers practical examples in R or Python.
A more advanced and theoretical counterpart to 'An Introduction to Statistical Learning,' this book provides a deep dive into the statistical underpinnings of machine learning. It valuable reference for researchers and practitioners seeking a thorough understanding of the algorithms. While mathematically rigorous, it is considered a classic in the field and is often used in graduate-level programs.
This comprehensive book covers both the theoretical and practical aspects of machine learning from a probabilistic perspective. It explores various algorithms and concepts rigorously, including Bayesian methods and neural networks. It well-regarded textbook for advanced undergraduate and graduate students and serves as a strong reference for researchers.
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Focuses on the practical aspects of building effective machine learning systems, offering guidance on making strategic decisions in ML projects. It is particularly valuable for those transitioning into or working as ML engineers or data scientists. It provides practical advice and best practices based on real-world experience.
Provides the essential mathematical background required for understanding machine learning algorithms, covering linear algebra, calculus, probability, and statistics. It is an excellent resource for students and professionals who need to solidify their mathematical foundations to better grasp the inner workings of ML models. It can be used as a prerequisite text or a companion resource.
Considered the standard textbook for reinforcement learning, this book covers foundational principles and real-world applications of RL. It is essential reading for anyone interested in this specific area of machine learning, which is crucial for developing agents that learn through interaction. It includes examples and connections to neuroscience.
Offers a practical, hands-on introduction to machine learning using the scikit-learn library in Python. It focuses on the practical aspects of applying ML algorithms and is suitable for data scientists and developers. It helps readers understand the core concepts and how to implement them effectively.
As the title suggests, this book provides a very basic and accessible introduction to machine learning for individuals with no prior background in coding, math, or statistics. It uses plain language and visuals to explain fundamental concepts and algorithms. This is an excellent starting point for complete newcomers to the field.
This book, written by the Stanford University professor and former Chief Scientist at Baidu, provides a clear overview of machine learning with lots of diagrams and hands-on practice.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/u3thsq/production
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Time
3 hours
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Authored by Google Cloud
This course belongs to a
collection
called
Advanced Machine Learning on Google Cloud. It's comprised of five courses:
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