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AWS Certified Machine Learning - Specialty (MLS-C01)
A Cloud Guru
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!
Register for this course and see more details by visiting:
OpenCourser.com/course/blwurq/aws
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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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AWS Certified Machine Learning - Specialty (MLS-C01).
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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
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.
Data Scientist
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.
Software Engineer
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.
Data 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.
Data Architect
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.
Data Analyst
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.
Cloud Architect
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.
Machine Learning Researcher
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.
Software Developer
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.
Applied Scientist
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.
Business Analyst
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.
Business Intelligence 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.
Product Manager
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.
Database Administrator
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.
Systems Engineer
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.
For more career information including salaries, visit:
OpenCourser.com/course/blwurq/aws
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).
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.
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.
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.
This textbook provides a probabilistic perspective on machine learning. It covers a wide range of topics, from Bayesian inference to deep learning.
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.
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.
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.
This textbook provides a comprehensive overview of machine learning, with a focus on the mathematical and statistical foundations of the field.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/blwurq/aws
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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
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