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Maryam Rezapoor,
Eva Pagneux,
Phu Nguyen,
Juno Lee,
and
Andrew Paster
Enroll in our free AWS machine learning course and get hands on experience using AWS. Learn how to build ML models and gain in-demand skills. Learn online with Udacity.
Register for this course and see more details by visiting:
OpenCourser.com/course/o24bbd/aws
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What's inside
Syllabus
Welcome to Udacity! We're excited to share more about your program and start this journey with you!
In this course, you will learn more about the pre-requisites, structure of the program, and getting
Read more
Syllabus
Welcome to Udacity! We're excited to share more about your program and start this journey with you!
In this course, you will learn more about the pre-requisites, structure of the program, and getting
Read more
In this lesson, you will learn the fundamentals of supervised and unsupervised machine learning, including the process steps of solving machine learning problems and explore several examples
In this lesson you will learn about some advanced machine learning techniques: computer vision, reinforcement learning, and generative AI. You will also learn about how to use AWS ML tools.
Learn software engineering practices and how they apply in data science. Part one covers clean and modular code, code efficiency, refactoring, documentation, and version control.
Learn software engineering practices and how they apply in data science. Part two covers testing code, logging, and conducting code reviews.
Learn the basics of object-oriented programming so that you can build your own Python package.
Congratulations on finishing your program!
Take the AWS Machine Learning Foundations Assessment Quiz!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Free tier accessible
Provides hands-on, pragmatic experiences with AWS
Builds in-demand skills in machine learning
Provides practical knowledge of software engineering principles
Covers a wide range of ML techniques, including computer vision, reinforcement learning, and generative AI
Save this course
Save AWS Machine Learning Foundations 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 Machine Learning Foundations with these
activities:
Join a Study Group or Online Discussion Forum
Show steps
Connect with fellow learners through study groups or online discussion forums. Sharing knowledge, asking questions, and engaging in discussions can enhance your learning experience.
Show steps
-
Find a study group or online forum dedicated to AWS Machine Learning.
-
Participate in discussions and ask questions.
Review Linear Algebra
Show steps
Review matrix operations, vector algebra, and other concepts from linear algebra that are foundational for machine learning.
Browse courses on
Linear Algebra
Show steps
-
Revisit basic concepts like vectors, matrices, and linear transformations.
-
Practice solving systems of linear equations.
Revisit Probability and Statistics
Show steps
Recall concepts such as probability distributions, statistical inference, and hypothesis testing, which provide a foundation for understanding machine learning algorithms.
Browse courses on
Probability
Show steps
-
Review probability theory, including Bayes' Theorem.
-
Practice applying statistical methods to real-world datasets.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore AWS Machine Learning Services
Show steps
Follow online tutorials and documentation to familiarize yourself with AWS machine learning services such as Amazon SageMaker and Amazon Machine Learning.
Show steps
-
Go through Amazon's official tutorials on using SageMaker and other services.
-
Explore code samples and examples provided by AWS.
Practice Building and Evaluating Machine Learning Models
Show steps
Engage in hands-on practice by building and evaluating machine learning models using AWS services, solidifying your understanding of the process.
Browse courses on
Machine Learning
Show steps
-
Use AWS SageMaker or Amazon Machine Learning to create and train models.
-
Evaluate model performance using metrics such as accuracy and F1 score.
-
Experiment with different model parameters and algorithms.
Write a Blog Post or Article on a Machine Learning Concept
Show steps
Deepen your understanding by writing a blog post or article that explains a machine learning concept. This activity encourages critical thinking and communication of technical ideas.
Show steps
-
Choose a machine learning concept or algorithm.
-
Research and understand the concept thoroughly.
-
Write a clear and concise explanation, including examples and illustrations.
Participate in an AWS Machine Learning Hackathon
Show steps
Put your skills to the test by participating in an AWS Machine Learning hackathon. This immersive experience fosters collaboration, problem-solving, and practical application of your learnings.
Show steps
-
Find an AWS Machine Learning hackathon that aligns with your interests.
-
Form a team or work independently.
-
Develop and submit a machine learning solution.
Join a Study Group or Online Discussion Forum
Show steps
Connect with fellow learners through study groups or online discussion forums. Sharing knowledge, asking questions, and engaging in discussions can enhance your learning experience.
Show steps
Show steps
Show steps
- Find a study group or online forum dedicated to AWS Machine Learning.
- Participate in discussions and ask questions.
Review Linear Algebra
Show steps
Review matrix operations, vector algebra, and other concepts from linear algebra that are foundational for machine learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revisit basic concepts like vectors, matrices, and linear transformations.
- Practice solving systems of linear equations.
Revisit Probability and Statistics
Show steps
Recall concepts such as probability distributions, statistical inference, and hypothesis testing, which provide a foundation for understanding machine learning algorithms.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review probability theory, including Bayes' Theorem.
- Practice applying statistical methods to real-world datasets.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore AWS Machine Learning Services
Show steps
Follow online tutorials and documentation to familiarize yourself with AWS machine learning services such as Amazon SageMaker and Amazon Machine Learning.
Show steps
Show steps
Show steps
- Go through Amazon's official tutorials on using SageMaker and other services.
- Explore code samples and examples provided by AWS.
Practice Building and Evaluating Machine Learning Models
Show steps
Engage in hands-on practice by building and evaluating machine learning models using AWS services, solidifying your understanding of the process.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Use AWS SageMaker or Amazon Machine Learning to create and train models.
- Evaluate model performance using metrics such as accuracy and F1 score.
- Experiment with different model parameters and algorithms.
Write a Blog Post or Article on a Machine Learning Concept
Show steps
Deepen your understanding by writing a blog post or article that explains a machine learning concept. This activity encourages critical thinking and communication of technical ideas.
Show steps
Show steps
Show steps
- Choose a machine learning concept or algorithm.
- Research and understand the concept thoroughly.
- Write a clear and concise explanation, including examples and illustrations.
Participate in an AWS Machine Learning Hackathon
Show steps
Put your skills to the test by participating in an AWS Machine Learning hackathon. This immersive experience fosters collaboration, problem-solving, and practical application of your learnings.
Show steps
Show steps
Show steps
- Find an AWS Machine Learning hackathon that aligns with your interests.
- Form a team or work independently.
- Develop and submit a machine learning solution.
Career center
Learners who complete AWS Machine Learning Foundations will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is a software professional who builds and maintains machine learning systems and applications. This role requires a strong foundation in computer science, software engineering, and machine learning algorithms. The AWS Machine Learning Foundations course can help you build a foundation in machine learning and gain hands-on experience using AWS ML tools. This course can help you prepare for a career as a Machine Learning Engineer by providing you with the skills and knowledge you need to succeed in this role.
Data Scientist
Data Scientist
A Data Scientist is a professional who uses data to solve business problems and make informed decisions. This role requires a strong foundation in statistics, machine learning, and data analysis. The AWS Machine Learning Foundations course can help you build a foundation in machine learning and gain hands-on experience using AWS ML tools. This course can help you prepare for a career as a Data Scientist by providing you with the skills and knowledge you need to succeed in this role.
Software Engineer
Software Engineer
A Software Engineer is a professional who designs, develops, and maintains software applications. This role requires a strong foundation in computer science and software engineering principles. The AWS Machine Learning Foundations course can help you build a foundation in machine learning and gain hands-on experience using AWS ML tools. This course can help you prepare for a career as a Software Engineer by providing you with the skills and knowledge you need to succeed in this role.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is a professional who uses mathematical and statistical models to analyze and predict financial data. This role requires a strong foundation in mathematics, statistics, and machine learning. The AWS Machine Learning Foundations course can help you build a foundation in machine learning and gain hands-on experience using AWS ML tools. This course can help you prepare for a career as a Quantitative Analyst by providing you with the skills and knowledge you need to succeed in this role.
Business Analyst
Business Analyst
A Business Analyst is a professional who analyzes business processes and data to identify opportunities for improvement. This role requires a strong foundation in business analysis and data analysis techniques. The AWS Machine Learning Foundations course can help you build a foundation in machine learning and gain hands-on experience using AWS ML tools. This course can help you prepare for a career as a Business Analyst by providing you with the skills and knowledge you need to succeed in this role.
Data Engineer
Data Engineer
A Data Engineer is a professional who designs and builds data pipelines and data infrastructure. This role requires a strong foundation in data engineering and data management techniques. The AWS Machine Learning Foundations course can help you build a foundation in machine learning and gain hands-on experience using AWS ML tools. This course can help you prepare for a career as a Data Engineer by providing you with the skills and knowledge you need to succeed in this role.
Machine Learning Researcher
Machine Learning Researcher
A Machine Learning Researcher is a professional who conducts research in the field of machine learning. This role requires a strong foundation in machine learning theory and algorithms. The AWS Machine Learning Foundations course can help you build a foundation in machine learning and gain hands-on experience using AWS ML tools. This course can help you prepare for a career as a Machine Learning Researcher by providing you with the skills and knowledge you need to succeed in this role.
Product Manager
Product Manager
A Product Manager is a professional who manages the development and launch of new products. This role requires a strong foundation in product management and marketing principles. The AWS Machine Learning Foundations course may be useful for Product Managers who want to learn more about machine learning and how it can be used to improve products and services.
Project Manager
Project Manager
A Project Manager is a professional who plans, executes, and tracks projects. This role requires a strong foundation in project management principles and techniques. The AWS Machine Learning Foundations course may be useful for Project Managers who want to learn more about machine learning and how it can be used to improve project outcomes.
Technical Writer
Technical Writer
A Technical Writer is a professional who writes technical documentation and training materials. This role requires a strong foundation in writing and editing. The AWS Machine Learning Foundations course may be useful for Technical Writers who want to learn more about machine learning and how it can be used to improve technical documentation.
Data Analyst
Data Analyst
A Data Analyst is a professional who analyzes data to identify trends and patterns. This role requires a strong foundation in data analysis techniques and tools. The AWS Machine Learning Foundations course may be useful for Data Analysts who want to learn more about machine learning and how it can be used to improve data analysis.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst is a professional who uses data to make informed business decisions. This role requires a strong foundation in business analysis and data analysis techniques. The AWS Machine Learning Foundations course may be useful for Business Intelligence Analysts who want to learn more about machine learning and how it can be used to improve business decision-making.
Statistician
Statistician
A Statistician is a professional who uses statistical methods to analyze data and make predictions. This role requires a strong foundation in statistics and probability theory. The AWS Machine Learning Foundations course may be useful for Statisticians who want to learn more about machine learning and how it can be used to improve statistical analysis.
Computer Scientist
Computer Scientist
A Computer Scientist is a professional who studies the theory and practice of computing. This role requires a strong foundation in computer science and mathematics. The AWS Machine Learning Foundations course may be useful for Computer Scientists who want to learn more about machine learning and how it can be used to solve complex problems.
Software Developer
Software Developer
A Software Developer is a professional who designs, develops, and maintains software applications. This role requires a strong foundation in software engineering principles and techniques. The AWS Machine Learning Foundations course may be useful for Software Developers who want to learn more about machine learning and how it can be used to improve software applications.
For more career information including salaries, visit:
OpenCourser.com/course/o24bbd/aws
Reading list
We've selected 12 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 Machine Learning Foundations.
Save
Comprehensive guide to deep learning, covering the latest techniques and applications. It valuable resource for anyone who wants to learn more about deep learning.
Provides a practical introduction to machine learning using Python. It covers the basics of machine learning, as well as more advanced topics such as deep learning and natural language processing.
Provides a probabilistic perspective on machine learning. It covers the foundations of probability theory and its applications to machine learning.
Provides a comprehensive overview of probabilistic graphical models. It covers the foundations of probability theory and its applications to machine learning.
Provides a comprehensive overview of reinforcement learning. It covers the foundations of reinforcement learning and its applications to a variety of problems.
Save
Provides a comprehensive overview of computer vision. It covers the foundations of computer vision and its applications to a variety of problems.
Provides a comprehensive overview of natural language processing with deep learning. It covers the foundations of NLP and its applications to a variety of problems.
Save
Provides a comprehensive overview of software engineering practices for data science. It covers the foundations of software engineering and its applications to data science.
Provides a comprehensive overview of statistical learning. It covers the foundations of statistical learning and its applications to a variety of problems.
Provides a practical introduction to machine learning for hackers. It covers the basics of machine learning and its applications to a variety of problems.
Provides a comprehensive overview of machine learning with R. It covers the foundations of machine learning and its applications to a variety of problems.
Provides a comprehensive overview of machine learning with Python. It covers the foundations of machine learning and its applications to a variety of problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/o24bbd/aws
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Effort
11 hours
Via
Udacity
Instructors
Maryam Rezapoor
Eva Pagneux
Phu Nguyen
Juno Lee
Andrew Paster
Language
English
Good to know
Free tier accessible
Provides hands-on, pragmatic experiences with AWS
Builds in-demand skills in machine learning
Provides practical knowledge of software engineering principles
Covers a wide range of ML techniques, including computer vision, reinforcement learning, and generative AI
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