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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!
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Machine Learning
Artificial Intelligence
AWS Cloud
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Register for this course and see more details by visiting:
OpenCourser.com/course/4vk2k4/introduction
Traffic lights
Traffic lights
Read about what's good
what should give you pause 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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Reviews summary
Practical machine learning on aws for beginners
According to learners, this course offers a highly practical and accessible introduction to Machine Learning on AWS, particularly beneficial for those new to ML or cloud-based AI services. Students consistently praise its focus on hands-on application and real-world solutions, enabling them to confidently use services like SageMaker, Rekognition, and Transcribe without deep theoretical knowledge. While generally considered a strong starting point, some reviewers note that the course does not delve into advanced concepts, making it less suitable for intermediate learners. A few students also mention minor discrepancies due to evolving AWS console UI changes, which occasionally require troubleshooting. Overall, it successfully demystifies ML on AWS for beginners.
Instructor explains concepts clearly and effectively.
"The instructor explains concepts clearly and the demos are useful, making it easy to follow along."
"The instructor's explanations were spot on and easy to follow; I loved the balance of theory and hands-on work."
"I found the instructor to be clear, making the content understandable even for complex topics."
Excellent for those new to ML or AWS.
"Highly recommended for beginners! This course really demystified the process for me."
"As a complete beginner to ML and AWS, this course built my confidence; the practical, step-by-step approach was perfect."
"I found this course to be good for beginners, covering the basics of AWS ML services well."
Focuses on practical application, not complex theory.
"This course is incredibly practical and hits the mark for anyone wanting to get started with ML on AWS without drowning in theory."
"The hands-on coding and projects are the strongest part of the course for me; it's truly 'practical' as advertised."
"I appreciated the focus on immediate application; the practical exercises were very helpful."
Occasional issues with outdated AWS console UI.
"Some of the AWS console UI has changed slightly since the videos were made, which can be a bit confusing at times, though still navigable."
"I had a few instances where I needed minor troubleshooting due to AWS UI changes, but it was manageable."
"Some parts felt a bit dated with the UI, which was annoying, but I could still follow along."
Not suited for intermediate or advanced learners.
"If you're looking for an overview, it's fine, but don't expect to become an expert... it's not for intermediates."
"I found this course to be too basic, hoping to learn more about deploying custom models on AWS, but it mostly covers high-level services."
"The course does not delve into the mathematical underpinnings of ML, which I suppose some might find superficial."
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 Introduction to Machine Learning on AWS with these
activities:
Organize Course Notes and Resources
Show steps
Enhance learning by organizing and reviewing course notes, assignments, quizzes, and exams, reinforcing key concepts and improving retention.
Show steps
-
Gather and organize course materials, including notes, slides, and assignments.
-
Review materials regularly to reinforce concepts.
-
Summarize key points and create study aids for future reference.
Participate in a Virtual Study Group for AWS Machine Learning
Show steps
Enhance understanding and retention by joining a virtual study group, where learners can collaborate, discuss course concepts, and support each other's progress.
Browse courses on
AWS Cloud
Show steps
-
Identify or join a virtual study group focused on AWS machine learning.
-
Regularly participate in group discussions and assignments.
-
Present findings and share knowledge within the group.
-
Collaborate on projects and provide feedback to peers.
Complete the AWS Machine Learning Specialization on Coursera
Show steps
Expand knowledge and gain a deeper understanding of AWS machine learning services by completing the comprehensive AWS Machine Learning Specialization on Coursera, providing structured guidance and real-world examples.
Browse courses on
AWS Cloud
Show steps
-
Enroll in the AWS Machine Learning Specialization on Coursera.
-
Complete the specialization's courses and assignments.
-
Obtain the Coursera certificate upon completion.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend an AWS Machine Learning Immersion Day
Show steps
Gain valuable insights and hands-on experience by attending an AWS Machine Learning Immersion Day, complementing the course content with expert guidance and practical applications.
Browse courses on
Cloud Computing
Show steps
-
Register and attend an AWS Machine Learning Immersion Day.
-
Actively participate in hands-on labs and expert sessions.
-
Network with industry professionals and AWS engineers.
Create a Machine Learning Model for Customer Segmentation
Show steps
Develop hands-on skills by creating a tailored machine learning model for customer segmentation, applying the concepts learned in the course.
Browse courses on
Machine Learning
Show steps
-
Define customer segments based on relevant criteria.
-
Collect and prepare customer data for analysis.
-
Choose an appropriate machine learning algorithm for the task.
-
Train and evaluate the model using AWS AI services.
-
Deploy and monitor the model to gain insights into customer behavior.
Document the ML Pipeline for a New Business Use Case
Show steps
Solidify understanding of the ML pipeline by documenting the process of implementing a new business use case, showcasing the practical applications of the course concepts.
Browse courses on
Machine Learning Pipeline
Show steps
-
Identify a specific business use case for machine learning.
-
Design and implement the ML pipeline using AWS services.
-
Create detailed documentation covering the pipeline architecture, data sources, models used, and evaluation metrics.
-
Publish and share the documentation for feedback and knowledge sharing.
Develop an AI-Powered Chatbot for Customer Service
Show steps
Apply course concepts to a real-world scenario by creating an AI-powered chatbot, demonstrating proficiency in implementing machine learning solutions for business applications.
Show steps
-
Design the chatbot's functionality and user interface.
-
Build the chatbot using appropriate AWS AI services.
-
Train the chatbot on relevant datasets to ensure accurate responses.
-
Deploy and test the chatbot in a simulated customer service environment.
Organize Course Notes and Resources
Show steps
Enhance learning by organizing and reviewing course notes, assignments, quizzes, and exams, reinforcing key concepts and improving retention.
Show steps
Show steps
Show steps
- Gather and organize course materials, including notes, slides, and assignments.
- Review materials regularly to reinforce concepts.
- Summarize key points and create study aids for future reference.
Participate in a Virtual Study Group for AWS Machine Learning
Show steps
Enhance understanding and retention by joining a virtual study group, where learners can collaborate, discuss course concepts, and support each other's progress.
Browse courses on
AWS Cloud
Show steps
Show steps
Show steps
- Identify or join a virtual study group focused on AWS machine learning.
- Regularly participate in group discussions and assignments.
- Present findings and share knowledge within the group.
- Collaborate on projects and provide feedback to peers.
Complete the AWS Machine Learning Specialization on Coursera
Show steps
Expand knowledge and gain a deeper understanding of AWS machine learning services by completing the comprehensive AWS Machine Learning Specialization on Coursera, providing structured guidance and real-world examples.
Browse courses on
AWS Cloud
Show steps
Show steps
Show steps
- Enroll in the AWS Machine Learning Specialization on Coursera.
- Complete the specialization's courses and assignments.
- Obtain the Coursera certificate upon completion.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend an AWS Machine Learning Immersion Day
Show steps
Gain valuable insights and hands-on experience by attending an AWS Machine Learning Immersion Day, complementing the course content with expert guidance and practical applications.
Browse courses on
Cloud Computing
Show steps
Show steps
Show steps
- Register and attend an AWS Machine Learning Immersion Day.
- Actively participate in hands-on labs and expert sessions.
- Network with industry professionals and AWS engineers.
Create a Machine Learning Model for Customer Segmentation
Show steps
Develop hands-on skills by creating a tailored machine learning model for customer segmentation, applying the concepts learned in the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define customer segments based on relevant criteria.
- Collect and prepare customer data for analysis.
- Choose an appropriate machine learning algorithm for the task.
- Train and evaluate the model using AWS AI services.
- Deploy and monitor the model to gain insights into customer behavior.
Document the ML Pipeline for a New Business Use Case
Show steps
Solidify understanding of the ML pipeline by documenting the process of implementing a new business use case, showcasing the practical applications of the course concepts.
Browse courses on
Machine Learning Pipeline
Show steps
Show steps
Show steps
- Identify a specific business use case for machine learning.
- Design and implement the ML pipeline using AWS services.
- Create detailed documentation covering the pipeline architecture, data sources, models used, and evaluation metrics.
- Publish and share the documentation for feedback and knowledge sharing.
Develop an AI-Powered Chatbot for Customer Service
Show steps
Apply course concepts to a real-world scenario by creating an AI-powered chatbot, demonstrating proficiency in implementing machine learning solutions for business applications.
Show steps
Show steps
Show steps
- Design the chatbot's functionality and user interface.
- Build the chatbot using appropriate AWS AI services.
- Train the chatbot on relevant datasets to ensure accurate responses.
- Deploy and test the chatbot in a simulated customer service environment.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Save
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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