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Introduction to Machine Learning on AWS

In this course, we start with some services where the training model and raw inference is handled for you by Amazon. We'll cover services which do the heavy lifting of computer vision, data extraction and analysis, language processing, speech recognition, translation, ML model training and virtual agents. You'll think of your current solutions and see where you can improve these solutions using AI, ML or Deep Learning. All of these solutions can work with your current applications to make some improvements in your user experience or the business needs of your application.

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What's inside

Syllabus

Week 1

Week 1 of this course introduces you to some artificial intelligence and machine learning terms. Then, you explore AWS machine learning services for computer vision, data extraction and analysis, and language processing.

Week 2

In week 2 of this course, you explore AWS machine learning services for speech recognition, language translation, and virtual agents.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores practical use cases for AI and ML, making it relevant to learners with hands-on experience

Focuses on industry-standard AI and ML services, making it suitable for learners who want to apply their skills in real-world scenarios

Introduces learners to a range of AWS machine learning services, providing a comprehensive foundation in cloud-based AI and ML

Provides examples of how AI, ML, and Deep Learning can enhance existing solutions, making it applicable to learners who want to improve their current applications

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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 Introduction to Machine Learning on AWS with these activities:

Review Python Basics

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Review the basics of Python programming to ensure you have a solid foundation before starting the course.

Browse courses on Python

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Review data types and variables
Practice creating and manipulating lists and dictionaries
Refresh your understanding of loops and conditional statements

Join a Study Group or Discussion Forum

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Engage with fellow students to share knowledge, ask questions, and enhance your understanding of the course material.

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Join a study group or online discussion forum dedicated to the course
Participate in discussions and ask questions to clarify concepts
Share your own insights and help others in the group

Follow Tutorials on AWS ML Services

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Explore tutorials provided by AWS to gain hands-on experience with the ML services covered in the course.

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Choose a service to explore, such as Amazon Rekognition or Amazon Translate
Follow the tutorials to create and test your own ML models
Experiment with different parameters and settings to optimize your models

Four other activities

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Create a Blog Post or Video Tutorial

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Solidify your learning by creating content that explains a concept or skill covered in the course.

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Choose a topic that you want to explain
Create a blog post or video tutorial that covers the topic in detail
Share your content with others and get feedback

Solve Coding Challenges on LeetCode

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Practice applying your ML knowledge by solving coding challenges related to the concepts covered in the course.

Browse courses on Coding Challenges

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Identify a coding challenge related to AI, ML, or Deep Learning
Attempt to solve the challenge on your own
Review solutions and discuss your approach with others in the course forum

Contribute to Open Source Machine Learning Projects

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Gain practical experience and contribute to the advancement of the ML field by participating in open source projects.

Browse courses on Machine Learning Projects

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Identify an open source project related to AI and machine learning
Review the project's documentation and codebase
Identify an area where you can contribute, such as bug fixing or feature development

Develop an ML Application Prototype

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Apply your knowledge of AWS ML services by creating a prototype of an ML application that addresses a real-world problem.

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Identify a problem or opportunity that can be solved with ML
Choose an appropriate AWS ML service and design your application architecture
Develop and train your ML model
Deploy and test your application

Career center

Learners who complete Introduction to Machine Learning on AWS will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists create machine learning models, machine learning systems, and other ML algorithms to solve complex business problems. They also clean and prepare data for analysis, create visualizations, and communicate insights to stakeholders. This course may be useful for Data Scientists seeking to expand their knowledge of AWS ML services.

See salaries and explore the career path for Data Scientist

Natural Language Processing Engineer

Natural Language Processing Engineers develop, implement, and maintain software for NLP applications. They work on NLP tasks such as text classification, text summarization, and machine translation. This course may be useful for Natural Language Processing Engineers seeking to expand their knowledge of AWS ML services.

See salaries and explore the career path for Natural Language Processing Engineer

Machine Learning Engineer

Machine Learning Engineers work on a team of software engineers, data engineers, and data scientists to deploy machine learning models into production. They design, develop, and maintain ML systems and ensure that models are performant, reliable, and scalable. This course may be useful for Machine Learning Engineers seeking to expand their knowledge of AWS ML services.

See salaries and explore the career path for Machine Learning Engineer

Computer Vision Engineer

Computer Vision Engineers develop, implement, and maintain software for computer vision applications. They work on computer vision tasks such as image recognition, object detection, and image segmentation. This course may be useful for Computer Vision Engineers seeking to expand their knowledge of AWS ML services.

See salaries and explore the career path for Computer Vision Engineer

Speech Recognition Engineer

Speech Recognition Engineers develop, implement, and maintain software for speech recognition applications. They work on speech recognition tasks such as automatic speech recognition, speaker recognition, and speech synthesis. This course may be useful for Speech Recognition Engineers seeking to expand their knowledge of AWS ML services.

See salaries and explore the career path for Speech Recognition Engineer

Product Manager

Product Managers work with stakeholders to define and develop product roadmaps. They conduct market research, analyze customer feedback, and make decisions about product features and functionality. This course may be useful for Product Managers seeking to expand their knowledge of AWS ML services and gain a better understanding of how to use ML to develop and improve products.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts work with stakeholders to identify business needs and develop solutions to meet those needs. They use data analysis, modeling, and other techniques to evaluate business processes and make recommendations for improvement. This course may be useful for Business Analysts seeking to expand their knowledge of AWS ML services and gain a better understanding of how to use ML to improve business processes.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. They work on a variety of software development tasks, including coding, debugging, and testing. This course may be useful for Software Engineers seeking to expand their knowledge of AWS ML services and gain a better understanding of how to use ML to develop software applications.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to identify patterns and trends. They use data analysis to solve business problems and make informed decisions. This course may be useful for Data Analysts seeking to expand their knowledge of AWS ML services and gain a better understanding of how to use ML to analyze data.

See salaries and explore the career path for Data Analyst

Project Manager

Project Managers plan, execute, and close projects. They work with stakeholders to define project scope, timelines, and budgets. They also track project progress and make sure that projects are completed on time and within budget. This course may be useful for Project Managers seeking to expand their knowledge of AWS ML services and gain a better understanding of how to use ML to manage projects.

See salaries and explore the career path for Project Manager

DevOps Engineer

DevOps Engineers work with software engineers and other stakeholders to ensure that software is developed and deployed quickly and efficiently. They use a variety of tools and techniques to automate software development and deployment processes. This course may be useful for DevOps Engineers seeking to expand their knowledge of AWS ML services and gain a better understanding of how to use ML to automate software development and deployment processes.

See salaries and explore the career path for DevOps Engineer

IT Manager

IT Managers plan, implement, and maintain IT systems. They work with senior management to align IT systems with business goals. They also work with other IT professionals to ensure that IT systems are secure, reliable, and scalable. This course may be useful for IT Managers seeking to expand their knowledge of AWS ML services and gain a better understanding of how to use ML to improve IT systems.

See salaries and explore the career path for IT Manager

Cloud Architect

Cloud Architects design, build, and maintain cloud computing systems. They work with customers to define cloud computing needs and develop solutions that meet those needs. They also work with cloud computing providers to ensure that cloud computing systems are secure, reliable, and scalable. This course may be useful for Cloud Architects seeking to expand their knowledge of AWS ML services and gain a better understanding of how to use ML to design and build cloud computing systems.

See salaries and explore the career path for Cloud Architect

Data Engineer

Data Engineers design, build, and maintain data pipelines. They work with data scientists and other stakeholders to ensure that data is clean, accessible, and secure. This course may be useful for Data Engineers seeking to expand their knowledge of AWS ML services and gain a better understanding of how to use ML to build and maintain data pipelines.

See salaries and explore the career path for Data Engineer

Database Administrator

Database Administrators design, implement, and maintain databases. They work with data engineers and other stakeholders to ensure that databases are performant, reliable, and scalable. This course may be useful for Database Administrators seeking to expand their knowledge of AWS ML services and gain a better understanding of how to use ML to improve database performance.

See salaries and explore the career path for Database Administrator

Reading list

We've selected 11 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.

Deep Learning

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Comprehensive reference on deep learning, covering both the theoretical foundations and practical applications of the field.

Introduction to Machine Learning on AWS

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What's inside

Syllabus

Good to know

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Activities

Career center

Reading list

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