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In this course, you get an overview of the concepts, terminology, and processes of the exciting field of machine learning!
Read more
In this course, you get an overview of the concepts, terminology, and processes of the exciting field of machine learning!
Read more
In this course, you get an overview of the concepts, terminology, and processes of the exciting field of machine learning!
What is machine learning? How can machine learning solve business problems? When is it appropriate to use a machine learning model? What are the phases of a machine learning pipeline? In this course, you get an overview of the concepts, terminology, and processes of the exciting field of machine learning!
Register for this course and see more details by visiting:
OpenCourser.com/course/84izdj/aws
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What's inside
Syllabus
AWS Foundations: Machine Learning Basics
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a concise introduction to the essential concepts, terms, and processes in machine learning with a focus on solving real-world business problems
Introduces the fundamental concepts and applications of machine learning, making it ideal for individuals with limited or no prior background in the field
Lays the foundational knowledge and skills necessary for further exploration and understanding of advanced machine learning topics
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Save AWS Foundations: Machine Learning Basics 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 AWS Foundations: Machine Learning Basics with these
activities:
Review AWS Fundamentals
Show steps
Reviewing AWS fundamentals before the course will help you set a strong foundation and better understand the concepts and principles of machine learning.
Show steps
-
Watch video tutorials on AWS services and architecture.
-
Follow along with AWS documentation and guides.
Experiment with Practical Exercises
Show steps
Hands-on practice will reinforce the concepts and techniques introduced in the course, enhancing your understanding and proficiency in machine learning.
Browse courses on
Machine Learning Algorithms
Show steps
-
Follow along with the course exercises and tutorials.
-
Build your own machine learning models using provided datasets.
-
Experiment with different algorithms and parameters.
-
Troubleshoot and debug your models.
Develop a Machine Learning Project
Show steps
Creating a project will challenge you to apply your knowledge, solve real-world problems, and enhance your problem-solving and technical skills in machine learning.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a problem or use case for machine learning.
-
Gather and prepare your dataset.
-
Train and evaluate your machine learning model.
-
Deploy your model and monitor its performance.
Two other activities
Expand to see all activities and additional details
Show all five activities
Participate in Machine Learning Challenges
Show steps
Participating in challenges will push you to apply your skills in a competitive environment, improve your problem-solving abilities, and gain recognition for your achievements.
Show steps
-
Identify and register for appropriate competitions.
-
Team up or work individually to solve challenges.
-
Submit solutions and track your performance.
-
Reflect on your results and identify areas for improvement.
Contribute to Open-Source Projects
Show steps
Contributing to open-source projects will enhance your understanding of machine learning principles, expose you to real-world applications, and build your programming and collaboration skills.
Show steps
-
Identify open-source machine learning projects that align with your interests.
-
Review the project documentation and contribute code or documentation.
-
Engage with the project community and contribute to discussions.
-
Seek mentorship from experienced contributors.
Review AWS Fundamentals
Show steps
Reviewing AWS fundamentals before the course will help you set a strong foundation and better understand the concepts and principles of machine learning.
Show steps
Show steps
Show steps
- Watch video tutorials on AWS services and architecture.
- Follow along with AWS documentation and guides.
Experiment with Practical Exercises
Show steps
Hands-on practice will reinforce the concepts and techniques introduced in the course, enhancing your understanding and proficiency in machine learning.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Follow along with the course exercises and tutorials.
- Build your own machine learning models using provided datasets.
- Experiment with different algorithms and parameters.
- Troubleshoot and debug your models.
Develop a Machine Learning Project
Show steps
Creating a project will challenge you to apply your knowledge, solve real-world problems, and enhance your problem-solving and technical skills in machine learning.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a problem or use case for machine learning.
- Gather and prepare your dataset.
- Train and evaluate your machine learning model.
- Deploy your model and monitor its performance.
Two other activities
Expand to see all activities and additional details
Show all five activities
Participate in Machine Learning Challenges
Show steps
Participating in challenges will push you to apply your skills in a competitive environment, improve your problem-solving abilities, and gain recognition for your achievements.
Show steps
Show steps
Show steps
- Identify and register for appropriate competitions.
- Team up or work individually to solve challenges.
- Submit solutions and track your performance.
- Reflect on your results and identify areas for improvement.
Contribute to Open-Source Projects
Show steps
Contributing to open-source projects will enhance your understanding of machine learning principles, expose you to real-world applications, and build your programming and collaboration skills.
Show steps
Show steps
Show steps
- Identify open-source machine learning projects that align with your interests.
- Review the project documentation and contribute code or documentation.
- Engage with the project community and contribute to discussions.
- Seek mentorship from experienced contributors.
Career center
Learners who complete AWS Foundations: Machine Learning Basics will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers lead the design, development, and deployment of machine learning models for their company's products and services. They must understand each phase of a machine learning pipeline. Experience with the processes described in this course is highly valuable for success in this role.
Data Scientist
Data Scientist
Data Scientists work on developing models that help their company optimize business decisions. They are expected to be familiar with the machine learning workflow, which this course can help build a foundational understanding of. They work with a team of analysts and engineers to craft and deploy solutions to real-world business problems.
Statistician
Statistician
Statisticians are expected to have strong analytical skills, comfort with data analysis, and expertise in machine learning concepts and techniques. This course can help build a strong foundation for a Statistician who is looking to refine their knowledge of foundational machine learning principles.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to help businesses make better decisions about investments. They need to be comfortable with machine learning concepts. This course can help build a foundation for success as a Quantitative Analyst by teaching foundational machine learning principles.
Data Analyst
Data Analyst
Data Analysts work with large datasets to extract insights and trends that inform business decisions. They are expected to know the fundamentals of machine learning. This course will help a Data Analyst understand the different phases of a machine learning pipeline.
Software Engineer
Software Engineer
A background in machine learning can be useful for Software Engineers seeking to work on machine learning products and services. This course provides an introduction to the field, covering foundational concepts and terminology that can power career growth for Software Engineers.
Data Engineer
Data Engineer
Data Engineers work on developing and maintaining the infrastructure that supports machine learning models. An understanding of the machine learning workflow can lead to success as a Data Engineer. This course will provide a high-level overview of the field and its processes.
Research Scientist
Research Scientist
Research Scientists who work in machine learning must have a strong theoretical and practical understanding of the field. The conceptual overview in this course can serve as an introduction to machine learning fundamentals for a Research Scientist who is new to the field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data and models to improve business processes. They need to understand the basics of machine learning to develop models that can optimize decision-making.
Financial Analyst
Financial Analyst
Financial Analysts use data and models to make investment recommendations. They need to understand the basics of machine learning to develop models that can predict financial performance.
Business Analyst
Business Analyst
A course on the basics of machine learning can be useful for Business Analysts who work on projects that involve machine learning models. This course will help provide a foundational understanding of the field.
Actuary
Actuary
Actuaries use data and models to assess financial risk. They need to understand the basics of machine learning to develop models that can predict future events.
Supply Chain Manager
Supply Chain Manager
Supply Chain Managers need to understand the basics of machine learning to implement machine learning solutions that improve the supply chain and reduce costs.
Product Manager
Product Manager
Product Managers develop and deliver products that meet user needs. They need to understand the fundamentals of machine learning to make informed decisions about product development and marketing. This course can help build a foundational knowledge of the field.
Marketing Manager
Marketing Manager
Marketing Managers need to be aware of the latest trends in machine learning to market their products and services effectively. This course can provide a basic understanding of machine learning concepts and terminology.
For more career information including salaries, visit:
OpenCourser.com/course/84izdj/aws
Reading list
We've selected 13 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
AWS Foundations: Machine Learning Basics.
Provides a hands-on introduction to machine learning using popular libraries like Scikit-Learn, Keras, and TensorFlow.
Save
Comprehensive and authoritative guide to deep learning, providing a deep understanding of the field.
Provides a probabilistic perspective on machine learning, which is essential for understanding the underlying principles of the field.
Provides a comprehensive overview of machine learning algorithms and techniques, with a focus on predictive data analytics.
Save
Provides an algorithmic perspective on machine learning, which is essential for understanding the underlying implementation of machine learning algorithms.
Classic textbook on pattern recognition and machine learning, providing a comprehensive overview of the field.
Provides a thorough and up-to-date overview of machine learning, covering a wide range of topics.
Practical guide to machine learning, providing hands-on experience with real-world datasets.
Comprehensive textbook on statistical learning, which provides a solid foundation for understanding the statistical principles behind machine learning.
Practical guide to machine learning for programmers, providing a hands-on introduction to the field.
Practical guide to deep learning using fastai and PyTorch, providing hands-on experience with real-world datasets.
Practical guide to machine learning using R, providing hands-on experience with real-world datasets.
Practical guide to machine learning using Python, providing hands-on experience with real-world datasets.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/84izdj/aws
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Time
28 hours
Level
Introductory
Via
Pluralsight
Instructor
AWS
Language
English
Good to know
Provides a concise introduction to the essential concepts, terms, and processes in machine learning with a focus on solving real-world business problems
Introduces the fundamental concepts and applications of machine learning, making it ideal for individuals with limited or no prior background in the field
Lays the foundational knowledge and skills necessary for further exploration and understanding of advanced machine learning topics
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