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Introduction to Machine Learning on AWS
Stephen Sennett
Stephen Sennett
Right now, machine learning (ML) and artificial intelligence (AI) are two of the most important skills in tech. They’re also among the most daunting. This course is about learning to use the AWS machine learning services in a practical manner. We’ll start with consuming some of the simple AI services, continue with some we can customize for our own use cases, and dive deeper into building our own bespoke solutions. Don’t worry, you aren’t going to be touching on mathematics or advanced ML concepts in this course; everything is practically focused on building powerful solutions without an expert background. But if you’re interested, you’ll be ready to explore the topic further!
Register for this course and see more details by visiting:
OpenCourser.com/course/4vk2k4/introduction
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners as it avoids advanced ML concepts and mathematics
Explores the practical implementation of AWS machine learning services, which aligns with industry standards
Provides hands-on labs and interactive materials, enhancing the learning experience
Develops professional skills in machine learning, which are highly relevant in both industry and academia
Course instruction is in English, which may limit accessibility for non-English speakers
Suitable for learners with no expert background in machine learning, making it accessible to a wider audience
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Introduction to Machine Learning on AWS.
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Career center
Learners who complete Introduction to Machine Learning on AWS will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops, deploys, and maintains machine learning models. This course offers a solid foundation for working with machine learning on AWS, which is an essential skillset for this role.
Data Scientist
Data Scientist
A Data Scientist combines knowledge of mathematics, statistics, and computing to extract insights from data. This course offers an introduction to machine learning, a core skillset for Data Scientists, and it may be particularly helpful for those who wish to transition to a Data Science role.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make informed decisions. This course can provide valuable insights into machine learning algorithms, which are increasingly used in data analysis today.
Health Data Analyst
Health Data Analyst
A Health Data Analyst collects, analyzes, and interprets health data to help improve patient care. This course may help build a foundation in machine learning, which is increasingly used in health care.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematics and statistics to improve business processes. This course may help build a foundation in machine learning, which is increasingly used in operations research.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematics and statistics to analyze data and make investment decisions. This course may help build a foundation in machine learning, which is increasingly used in quantitative analysis.
Biostatistician
Biostatistician
A Biostatistician uses mathematics and statistics to analyze biological data. This course may help build a foundation in machine learning, which is increasingly used in biostatistics.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs and implements AI systems. This course may be a good starting point for those who wish to work with AI, as it introduces foundational skills and concepts.
Research Analyst
Research Analyst
A Researcher Analyst conducts research and analysis to help businesses make informed decisions. This course may help build a foundation in machine learning, which is increasingly used in research.
Cloud Architect
Cloud Architect
A Cloud Architect designs and manages cloud computing systems. AWS is a major player in this industry, so this course may be helpful for developing the foundational knowledge to design machine learning solutions on AWS.
Business Analyst
Business Analyst
A Business Analyst uses data and analysis to help businesses improve their operations. This course may help build a foundation in machine learning, which is increasingly used in business analysis.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment decisions. This course may help build a foundation in machine learning, which is increasingly used in finance.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course helps build a foundation by introducing fundamental principles of machine learning, which are essential for writing effective code.
Marketing Analyst
Marketing Analyst
A Marketing Analyst uses data and analysis to help businesses improve their marketing campaigns. This course may help build a foundation in machine learning, which is increasingly used in marketing.
Product Manager
Product Manager
A Product Manager is responsible for managing the development and launch of products. This course can provide insights into how machine learning is used to build and improve products, which may be useful for those who wish to manage ML-related products.
For more career information including salaries, visit:
OpenCourser.com/course/4vk2k4/introduction
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
Introduction to Machine Learning on AWS.
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Provides a comprehensive overview of deep learning, with a focus on the mathematical and theoretical foundations of DL. It good choice for those who want to understand the underlying principles of DL, or for those who want to prepare for a career in DL research.
Provides a comprehensive overview of reinforcement learning, with a focus on the mathematical and theoretical foundations of RL. It good choice for those who want to understand the underlying principles of RL, or for those who want to prepare for a career in RL research.
Provides a comprehensive overview of machine learning with Python, with a focus on the practical aspects of ML. It good choice for those who want to learn how to use ML in Python to solve real-world problems.
Provides a comprehensive overview of machine learning in finance, with a focus on the applications of ML to financial modeling and trading. It good choice for those who want to learn how to use ML to improve the performance of financial models.
Provides a comprehensive overview of machine learning for marketing, with a focus on the applications of ML to improve the effectiveness of marketing campaigns. It good choice for those who want to learn how to use ML to improve the reach and impact of their marketing efforts.
Provides a comprehensive overview of machine learning for manufacturing, with a focus on the applications of ML to improve the efficiency and productivity of manufacturing processes. It good choice for those who want to learn how to use ML to improve the profitability of their manufacturing operations.
Provides a comprehensive overview of machine learning for systems and control, with a focus on the applications of ML to control theory. It good choice for those who want to learn how to use ML to improve the performance of control systems.
Provides a gentle introduction to machine learning, with a focus on the basics. It good starting point for those new to machine learning, or for those who want to brush up on the fundamentals.
Provides a non-technical introduction to machine learning, with a focus on the concepts and applications of ML. It good choice for those who want to learn about ML without getting bogged down in the technical details.
Provides a non-technical introduction to artificial intelligence, with a focus on the concepts and applications of AI. It good choice for those who want to learn about AI without getting bogged down in the technical details.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4vk2k4/introduction
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Time
60 hours
Level
Advanced
Via
Pluralsight
Instructor
Stephen Sennett
Language
English
Good to know
Builds a strong foundation for beginners as it avoids advanced ML concepts and mathematics
Explores the practical implementation of AWS machine learning services, which aligns with industry standards
Provides hands-on labs and interactive materials, enhancing the learning experience
Develops professional skills in machine learning, which are highly relevant in both industry and academia
Course instruction is in English, which may limit accessibility for non-English speakers
Suitable for learners with no expert background in machine learning, making it accessible to a wider audience
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