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AWS Certified Machine Learning - Specialty (MLS-C01)

A Cloud Guru

Hello Cloud Gurus, Your executive board members are asking you to do something with it. Your grandmother is asking you if it will put you out of a job. Your deadbeat college roommate is asking if you can help him find a date with it. Everyone seems to fuss over Machine Learning, but how many of us truly understand it? Too few. Fortunately, ACG has your back yet again with a fresh course focused on helping you outsmart the new AWS Certified Machine Learning Specialty. In typical ACG manner, we have created a course that confronts the potentially dull and boring topic of machine learning head-on with quirky and engaging lectures, interactive labs and plenty of real-world, plain-speak examples. In this course, you’ll learn: - The domains of knowledge for the AWS Certified Machine Learning Speciality exam. - Best practices for using the tools and platforms of AWS for data engineering, data analysis, machine learning modeling, model evaluation and deployment. - Hands-on labs designed to challenge your intuition, creativity and knowledge of the AWS platform. With this course you'll get a solid understanding of the services and platforms available on AWS for Machine Learning projects, build a foundation to pass the certification exam and feel equipped to use the AWS ML portfolio in your own real-world applications. Don’t just sit idly by, watching as robotic overlords take over the world. Create your own army of sentient machines and beat them at their own game! And keep being awesome, cloud gurus!

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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Focuses on the AWS Certified Machine Learning Specialty certification exam
Teaches learners how to use AWS tools and platforms for data engineering, analysis, modeling, evaluation, and deployment
Taught by A Cloud Guru, who are recognized for their work in cloud computing
Includes hands-on labs that help learners apply theory in practice
Covers the domains of knowledge for the AWS Certified Machine Learning Specialty exam
Develops skills and knowledge that prepare learners to use AWS' ML portfolio in real-world 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 AWS Certified Machine Learning - Specialty (MLS-C01) with these activities:
Compile a list of machine learning resources
Expand your knowledge base by compiling a list of useful machine learning resources.
Browse courses on AWS Machine Learning
Show steps
  • Search for online resources on machine learning
  • Categorize the resources based on topic or type
Review Python Basics
Reviewing Python basics will help you refresh your programming knowledge and prepare you for the hands-on labs in this course.
Browse courses on Python
Show steps
  • Review variables, data types, and operators.
  • Practice writing simple Python programs.
Review Python syntax
Refresh your understanding of Python syntax to prepare for the course.
Browse courses on Python
Show steps
  • Go through a Python tutorial to review basic syntax
  • Practice writing simple Python programs
23 other activities
Expand to see all activities and additional details
Show all 26 activities
Review the Basics of Data Science
Refreshes and revises essential data science fundamentals you need to succeed in this course
Browse courses on Data Science Fundamentals
Show steps
  • Review key concepts in data science, such as data types, data structures, and data analysis techniques.
  • Practice working with data using a programming language such as Python or R.
  • Complete online tutorials or exercises to reinforce your understanding.
Mentor Junior Students or Beginner Learners
Enhances your own understanding by teaching and supporting others
Browse courses on Mentoring
Show steps
  • Identify opportunities to mentor students who are new to machine learning.
  • Share your knowledge and experiences through one-on-one sessions or group discussions.
  • Provide feedback and guidance to help mentees develop their skills.
Practice AWS CLI commands
Improve your proficiency in using AWS CLI commands.
Browse courses on AWS CLI
Show steps
  • Find a list of common AWS CLI commands
  • Practice using the commands in a terminal
Understand the AWS Cloud and ML Terms
Review the fundamental concepts of AWS cloud services and machine learning to enhance your understanding of the course material.
Browse courses on Cloud Computing
Show steps
  • Define key cloud computing and AWS concepts
  • Explore different types of ML and their applications
  • Identify essential AWS ML services and their capabilities
Attend an AWS Machine Learning Workshop
Attend workshops to gain insights from experts and engage with industry professionals.
Show steps
  • Research and identify an AWS Machine Learning workshop that aligns with your interests
  • Register and attend the workshop
  • Actively participate, ask questions, and network with other attendees
Join a study group
Collaborate with other students to discuss concepts and reinforce your understanding.
Show steps
  • Find or create a study group with other students taking the course
  • Meet regularly to discuss course material, share insights, and work on projects together
Participate in Peer-to-Peer Learning and Discussion
Fosters collaboration and knowledge sharing among peers
Show steps
  • Join or create a peer learning group or discussion forum.
  • Engage in discussions, share knowledge, and ask questions.
  • Collaborate on projects or study materials.
Connect with ML Practitioners
Attend industry meetups, conferences, or online forums to connect with professionals in the field and gain insights into real-world ML applications.
Show steps
  • Identify and join local ML communities or online groups
  • Participate in discussions and ask questions
  • Network with ML practitioners to learn about their experiences and career paths
Solve Machine Learning Practice Problems
Practice solving problems to reinforce your understanding of machine learning concepts and techniques.
Browse courses on Machine Learning Algorithms
Show steps
  • Choose a practice problem set related to the course topics
  • Attempt to solve the problems on your own
  • Review your solutions and identify areas for improvement
Follow Guided Tutorials on AWS Machine Learning Services
Provides practical, hands-on experience with the AWS Machine Learning platform
Show steps
  • Identify the specific AWS Machine Learning service you want to learn.
  • Find and follow guided tutorials provided by AWS or other reputable sources.
  • Complete the exercises and examples in the tutorials to practice using the service.
Attend AWS Workshops or Webinars
Participate in official workshops or webinars organized by AWS to delver deeper into specific ML topics and learn from industry experts.
Show steps
  • Identify relevant AWS ML workshops or webinars
  • Register and attend the selected events
  • Take notes and engage actively during the sessions
Mentorship and Discussion
By sharing your knowledge and helping others, you can deepen your own understanding and sharpen your skills.
Show steps
  • Connect with a peer or junior colleague who is learning machine learning
  • Offer guidance, answer questions, and share your experiences
  • Engage in discussions to exchange ideas and perspectives
Follow AWS tutorials on machine learning
Enhance your understanding of AWS machine learning by following guided tutorials.
Browse courses on AWS Machine Learning
Show steps
  • Find AWS tutorials on machine learning
  • Follow the tutorials and complete the exercises
Complete AWS Machine Learning Tutorials
Follow guided tutorials to gain hands-on experience with AWS Machine Learning services and tools.
Show steps
  • Identify a specific AWS Machine Learning service or feature you want to learn
  • Find an official AWS tutorial or documentation guide
  • Follow the steps and complete the exercises
Create a machine learning model
Solidify your understanding of machine learning by creating a model using AWS.
Browse courses on Machine Learning Model
Show steps
  • Choose a dataset and define the problem statement
  • Select the appropriate AWS machine learning service and create an instance
  • Train and evaluate the model
  • Deploy the model
Follow AWS Machine Learning tutorials
Provides visual aids, step-by-step instructions, and hands-on experience for building proficiency with AWS Machine Learning services.
Browse courses on AWS Machine Learning
Show steps
  • Identify relevant tutorials
  • Follow the tutorials step-by-step
  • Complete the exercises included in the tutorials
Practice Hands-on Labs
Engage in hands-on exercises to reinforce your understanding of the AWS ML process and gain practical experience in implementing ML solutions.
Browse courses on Data Engineering
Show steps
  • Complete the provided AWS ML hands-on labs
  • Build your own ML models and pipelines
  • Experiment with different ML algorithms and parameters
Launch a Personal Machine Learning Project
Fosters creativity and independent learning by applying machine learning in a self-directed project
Browse courses on Machine Learning Projects
Show steps
  • Brainstorm and select a project idea that aligns with your interests.
  • Define the scope and objectives of your project.
  • Gather and prepare the necessary data.
  • Choose and apply appropriate machine learning algorithms.
  • Evaluate the performance of your model and make adjustments as needed.
Build a Machine Learning Model for a Real-World Problem
Engage in a practical project to apply your skills and gain experience in the real-world application of machine learning.
Show steps
  • Define a specific problem or task that you want to solve
  • Gather and prepare the necessary data
  • Choose and train a machine learning model
  • Evaluate the model's performance and make adjustments as needed
  • Deploy the model and monitor its performance
Complete practice questions
Engages critical thinking skills, reinforces knowledge, and builds problem-solving abilities specifically related to the course's content.
Browse courses on Machine Learning
Show steps
  • Identify areas needing improvement
  • Practice questions.
  • Review solutions thoroughly
Build a Machine Learning Model for a Real-World Application
Demonstrates your ability to apply machine learning concepts to solve real-world problems
Browse courses on Machine Learning Projects
Show steps
  • Define a specific problem or application that you want to address with machine learning.
  • Gather and prepare the necessary data.
  • Select and train a machine learning model using AWS Machine Learning services.
  • Deploy the model and evaluate its performance.
  • Document your project, including the problem definition, data analysis, model selection, and evaluation results.
Create a machine learning model
Enhances understanding of machine learning algorithms, data preparation, and model evaluation through hands-on application.
Browse courses on Machine Learning Model
Show steps
  • Gather and clean data
  • Choose and train a machine learning model
  • Evaluate the model's performance
  • Deploy the model
Build a portfolio of machine learning projects
Provides a tangible showcase of skills, boosts confidence, and aids in career prospects by demonstrating practical implementation.
Browse courses on Portfolio
Show steps
  • Identify potential projects
  • Plan and execute the projects
  • Document and present the outcomes

Career center

Learners who complete AWS Certified Machine Learning - Specialty (MLS-C01) will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer is someone who does work very similar to a Data Scientist. Machine Learning Engineers and Data Scientists often have similar backgrounds and can sometimes be found working on the same sorts of projects. However, in general, Machine Learning Engineers are more focused on designing, implementing, and testing Machine Learning models than doing the related work that a Data Scientist often does. This course provides Machine Learning Engineers the real-world and plain-speak examples needed to develop intuition for how to design and implement AWS Machine Learning models and build a strong foundation for your Machine Learning Engineering career.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
A Data Scientist is very interested in the services, platforms, and tools that AWS offers for boosting the development, deployment, and use of Machine Learning. AWS offers such a wide range of tools that a Data Scientist can use for data engineering, data analysis, machine learning modeling, model evaluation, and deployment that it may be challenging for a Data Scientist to know where to even begin. This course helps make the learning of AWS Machine Learning tools accessible and quite manageable by organizing the learning into modules that correspond to the domains of knowledge needed to ace the AWS Certified Machine Learning Specialty exam.
See salaries and explore the career path for Data Scientist
Software Engineer
Software Engineers who work on data-intensive or AI-powered applications should take this course to gain a deep understanding of the tools and platforms available in AWS for data engineering, data analysis, machine learning modeling, model evaluation, and deployment. An AWS tool that is well suited for your application can make your life as a Software Engineer much easier and improve productivity. This course discusses just about all of the services and platforms for Machine Learning that AWS has to offer, so you will be able to select the ones that are most relevant to the software applications and systems that you are designing and developing once you complete this course.
See salaries and explore the career path for Software Engineer
Data Engineer
This course will be of great use to Data Engineers who wish to develop a deep understanding of the tools and platforms available in AWS for data engineering, data analysis, machine learning modeling, model evaluation, and deployment. This course may help you advance your career or serve as a good introduction or refresher for those who wish to pivot into becoming a Data Engineer.
See salaries and explore the career path for Data Engineer
Data Architect
A Data Architect who is fluent in Machine Learning is a very valuable asset to any organization. This course may help to land you this role or advance your career in this role, as it covers best practices for using the tools and platforms of AWS for data engineering, data analysis, machine learning modeling, model evaluation, and deployment.
See salaries and explore the career path for Data Architect
Data Analyst
Data Analysts may not have a heavy focus on Machine Learning, depending on their particular job responsibilities, but the fact of the matter is that many employers are increasingly expecting Data Analysts to have some level of fluency in Machine Learning. This course is an efficient and effective way to gain fluency and build a strong foundation in Machine Learning and the AWS tools and platforms for working with data. The course covers the domains of knowledge for the AWS Certified Machine Learning Specialty exam, so you will become very familiar with the key tools and platforms that exist in AWS for Machine Learning and related purposes.
See salaries and explore the career path for Data Analyst
Cloud Architect
This course can be very helpful for Cloud Architects who wish to gain knowledge and skills for designing and implementing AWS Machine Learning solutions. It provides coverage of the domains of knowledge for the AWS Certified Machine Learning Specialty exam, as well as hands-on labs that are designed to challenge your intuition, creativity, and knowledge of the AWS platform. These hands-on labs help you to develop the intuition you will need to be an effective Cloud Architect.
See salaries and explore the career path for Cloud Architect
Machine Learning Researcher
This course may be very useful to Machine Learning Researchers who wish to gain hands-on experience with AWS Machine Learning tools and platforms. Course domains are aligned with those for the AWS Certified Machine Learning Specialty exam, so you will learn about all of the major services, platforms, and tools for Machine Learning that AWS offers.
See salaries and explore the career path for Machine Learning Researcher
Software Developer
This course can be helpful for Software Developers who wish to develop a deep understanding of the services and platforms offered by AWS for data engineering, data analysis, machine learning modeling, model evaluation, and deployment. As a Software Developer, this course can help you to build a strong foundation for developing data-intensive applications and systems on AWS.
See salaries and explore the career path for Software Developer
Applied Scientist
Applied Scientists will greatly benefit from this course. This course can help you develop the skills you need to work with AWS Machine Learning tools and become familiar with the best practices for using these tools. Course domains are aligned with those for the AWS Certified Machine Learning Specialty exam, so you will get a broad overview of the landscape of AWS Machine Learning products.
See salaries and explore the career path for Applied Scientist
Business Analyst
It is becoming increasingly valuable for Business Analysts to have some fluency in Machine Learning, as Machine Learning is quickly becoming the gold standard for automating and streamlining data analysis. This course may help you build some momentum toward a career pivot into the field of Data Science. At the very least, this course can help you become better prepared to understand and communicate your findings to Data Scientists and other technical staff.
See salaries and explore the career path for Business Analyst
Business Intelligence Analyst
This course may be helpful for Business Intelligence Analysts who wish to gain some knowledge and skills that will help them work more closely and effectively with Data Scientists and other technical professionals who are making use of AWS for Machine Learning.
See salaries and explore the career path for Business Intelligence Analyst
Product Manager
This course may be helpful to Product Managers who wish to learn more about AWS Machine Learning. This course will help you understand the value and potential impact of Machine Learning, as well as the tools and platforms that AWS offers for deploying Machine Learning solutions. Product Managers who have some fluency in Machine Learning will be better able to make good decisions about when and how to incorporate Machine Learning into their products.
See salaries and explore the career path for Product Manager
Database Administrator
This course may be helpful to Database Administrators who wish to gain some knowledge about AWS Machine Learning and some best practices for using AWS Machine Learning tools and platforms for using with databases.
See salaries and explore the career path for Database Administrator
Systems Engineer
This course may be helpful to Systems Engineers who wish to gain some knowledge and skills that will help them work more closely and effectively with Data Scientists and other technical professionals who are making use of AWS for Machine Learning.
See salaries and explore the career path for Systems Engineer

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 AWS Certified Machine Learning - Specialty (MLS-C01).
Cover image
Deep Learning
Save
This textbook provides a comprehensive overview of deep learning. It covers a wide range of topics, from the basics of deep learning to advanced topics such as generative adversarial networks and reinforcement learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
This practical guide to machine learning with Python provides a comprehensive overview of the scikit-learn, Keras, and TensorFlow libraries. It covers a wide range of topics, from data preprocessing to model deployment.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Deep Learning with PyTorch
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Provides a comprehensive overview of machine learning with PyTorch. It covers a wide range of topics, from the basics of machine learning to advanced topics such as deep learning and natural language processing.
Deep Learning with PyTorch: Build, train, and tune...
Paperback
$$
Deep Learning with PyTorch: Build, train, and tune...
Kindle Edition
$$
Cover image
Probabilistic Machine Learning
Save
This textbook provides a probabilistic perspective on machine learning. It covers a wide range of topics, from Bayesian inference to deep learning.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
Reinforcement Learning, second edition
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This textbook provides a comprehensive overview of reinforcement learning. It covers a wide range of topics, from the basics of reinforcement learning to advanced topics such as multi-agent reinforcement learning and deep reinforcement learning.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Machine Learning with PyTorch and Scikit-Learn
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This comprehensive guide to machine learning with Python covers the fundamentals of machine learning, data preparation, model selection, and evaluation. It also includes hands-on labs and exercises to reinforce learning.
Machine Learning with PyTorch and Scikit-Learn:...
Kindle Edition
$$
Cover image
Cross-Lingual Word Embeddings
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This comprehensive guide to deep learning for natural language processing covers the fundamentals of deep learning, as well as specific applications to NLP tasks such as text classification, sentiment analysis, and machine translation.
Cross-Lingual Word Embeddings (Synthesis Lectures...
Paperback
$$
Cover image
Machine Learning, revised and updated edition
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This textbook provides a comprehensive overview of machine learning, with a focus on the mathematical and statistical foundations of the field.
Machine Learning, revised and updated edition (The...
Paperback
$$
Machine Learning, revised and updated edition (The...
Kindle Edition
$$
Cover image
Machine Learning with R
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Provides a comprehensive overview of machine learning with R. It covers a wide range of topics, from the basics of machine learning to advanced topics such as deep learning and natural language processing.
Machine Learning with R - Fourth Edition: Learn...
Paperback
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Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
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(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
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R: Unleash Machine Learning Techniques
Kindle Edition
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Machine Learning with R: Learn How to Use R to...
Paperback
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(中文) Machine learning and R language (3rd edition of the...
Paperback
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Cover image
Pattern Classification
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Provides a comprehensive overview of machine learning with Java. It covers a wide range of topics, from the basics of machine learning to advanced topics such as deep learning and natural language processing.
Pattern Classification
Hardcover
$$$
Pattern Classification
Paperback
$$
R. O. Duda's P. E. Hart's D. G. Stork's Pattern...
Unknown Binding
$$$$
Pattern Recognition and Machine Learning
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This classic textbook provides a comprehensive and mathematical treatment of machine learning. It valuable resource for students and researchers interested in the theoretical foundations of machine learning.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
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Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$

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Course image
Time
1050 hours
Level
Graduate
Via
Pluralsight
Instructor
A Cloud Guru
Language
English
Good to know
Focuses on the AWS Certified Machine Learning Specialty certification exam
Teaches learners how to use AWS tools and platforms for data engineering, analysis, modeling, evaluation, and deployment
Taught by A Cloud Guru, who are recognized for their work in cloud computing
Includes hands-on labs that help learners apply theory in practice
Covers the domains of knowledge for the AWS Certified Machine Learning Specialty exam
Develops skills and knowledge that prepare learners to use AWS' ML portfolio in real-world applications
Share and help others discover this course.
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