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AWS
This course will walk you through the major stages of a typical ML pipeline, including problem framing, data cleaning, data visualization and analysis, functional engineering, and model training and evaluation.
Read more
This course will walk you through the major stages of a typical ML pipeline, including problem framing, data cleaning, data visualization and analysis, functional engineering, and model training and evaluation.
Read more
This course will walk you through the major stages of a typical ML pipeline, including problem framing, data cleaning, data visualization and analysis, functional engineering, and model training and evaluation.
This course will walk you through the major stages of a typical ML pipeline, including problem framing, data cleaning, data visualization and analysis, functional engineering, and model training and evaluation. As a use case, we'll cover the key concepts and processes that have been implemented throughout Amazon's pipeline. Throughout the session, we will introduce different types of ML problems and the different categories of ML algorithms available. This training will familiarize you with the concept of phases in the ML pipeline and familiarize you with the key terms and definitions involved.
Register for this course and see more details by visiting:
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What's inside
Syllabus
The Machine Learning Process
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines the core stages of a typical ML pipeline, including problem framing, data cleaning, and model training, which is standard in industry
Taught by AWS, who are recognized for their work in cloud computing and ML
Develops functional engineering skills, which are essential for building and deploying ML models
Provides a multi-modal learning experience with videos, readings, and hands-on labs
Requires learners to have a basic understanding of ML concepts
Does not cover advanced ML techniques or algorithms
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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 The Machine Learning Process with these
activities:
Review Linear Algebra Concepts
Show steps
Ensure you have a strong foundation in linear algebra, which is essential for many machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
-
Review textbooks and online resources.
-
Practice solving linear algebra problems.
Seek Mentorship
Show steps
Connect with experienced professionals to gain guidance and support.
Browse courses on
Mentoring
Show steps
-
Identify potential mentors in your field.
-
Reach out and request mentorship.
Peer Code Review
Show steps
Engage in peer feedback and improve the quality of your code.
Browse courses on
Peer Review
Show steps
-
Find a peer with similar skills and interests.
-
Review each other's code and provide constructive feedback.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Coding Kata Practice
Show steps
Engage in deliberate practice to improve problem-solving and coding skills.
Browse courses on
Software Development
Show steps
-
Select a coding kata or exercise.
-
Implement a solution in your preferred programming language.
-
Test and refine your solution.
AWS CLI Practicals
Show steps
Gain hands-on experience with the AWS command-line interface.
Show steps
-
Set up the AWS CLI on your local machine.
-
Execute basic commands to manage AWS resources.
-
Practice troubleshooting common CLI issues.
Contribute to Open Source Projects
Show steps
Get involved in open source communities to expand your technical skills and contribute to the broader developer ecosystem.
Browse courses on
Open Source
Show steps
-
Find open source projects that align with your interests.
-
Review their documentation and codebase.
-
Identify areas where you can contribute.
-
Submit pull requests with your proposed changes.
Data Visualization Project
Show steps
Develop your ability to effectively present and communicate data insights.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset of interest.
-
Explore and clean the data.
-
Select appropriate visualization techniques.
-
Create interactive or static visualizations.
Machine Learning Model Deployment
Show steps
Gain practical experience in deploying and managing ML models.
Browse courses on
Machine Learning Deployment
Show steps
-
Choose a cloud platform or hosting provider.
-
Containerize your ML model.
-
Deploy the model to the target platform.
-
Monitor and maintain the deployed model.
Review Linear Algebra Concepts
Show steps
Ensure you have a strong foundation in linear algebra, which is essential for many machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review textbooks and online resources.
- Practice solving linear algebra problems.
Seek Mentorship
Show steps
Connect with experienced professionals to gain guidance and support.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify potential mentors in your field.
- Reach out and request mentorship.
Peer Code Review
Show steps
Engage in peer feedback and improve the quality of your code.
Browse courses on
Peer Review
Show steps
Show steps
Show steps
- Find a peer with similar skills and interests.
- Review each other's code and provide constructive feedback.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Coding Kata Practice
Show steps
Engage in deliberate practice to improve problem-solving and coding skills.
Browse courses on
Software Development
Show steps
Show steps
Show steps
- Select a coding kata or exercise.
- Implement a solution in your preferred programming language.
- Test and refine your solution.
AWS CLI Practicals
Show steps
Gain hands-on experience with the AWS command-line interface.
Show steps
Show steps
Show steps
- Set up the AWS CLI on your local machine.
- Execute basic commands to manage AWS resources.
- Practice troubleshooting common CLI issues.
Contribute to Open Source Projects
Show steps
Get involved in open source communities to expand your technical skills and contribute to the broader developer ecosystem.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find open source projects that align with your interests.
- Review their documentation and codebase.
- Identify areas where you can contribute.
- Submit pull requests with your proposed changes.
Data Visualization Project
Show steps
Develop your ability to effectively present and communicate data insights.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset of interest.
- Explore and clean the data.
- Select appropriate visualization techniques.
- Create interactive or static visualizations.
Machine Learning Model Deployment
Show steps
Gain practical experience in deploying and managing ML models.
Browse courses on
Machine Learning Deployment
Show steps
Show steps
Show steps
- Choose a cloud platform or hosting provider.
- Containerize your ML model.
- Deploy the model to the target platform.
- Monitor and maintain the deployed model.
Career center
Learners who complete The Machine Learning Process will develop knowledge and skills
that may be useful to these careers:
Market Researcher
Market Researcher
Market Researchers collect and analyze data about consumers and markets. They use their findings to help businesses make informed decisions about product development, marketing, and pricing. This course, which covers data cleaning, data visualization, and analysis, may be useful for prospective Market Researchers looking to gain a strong foundation in the field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and maintaining ML systems. They work closely with Data Scientists to ensure that ML models are production-ready and meet the needs of the business. This course, which provides a comprehensive overview of the ML pipeline, may be useful for prospective Machine Learning Engineers looking to gain a strong foundation in the field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. They work in a variety of industries, including finance, healthcare, and insurance. This course, which covers data cleaning, data visualization, and analysis, may be useful for prospective Quantitative Analysts looking to gain a strong foundation in the field.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This course, which covers data cleaning, data visualization, and analysis, may be useful for prospective Actuaries looking to gain a strong foundation in the field.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease and injury in populations. They work with public health officials to develop and implement programs to prevent and control disease. This course, which covers data cleaning, data visualization, and analysis, may be useful for prospective Epidemiologists looking to gain a strong foundation in the field.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in the biomedical sciences. They work with doctors, researchers, and other healthcare professionals to design and analyze studies, and to interpret results. This course, which covers data cleaning, data visualization, and analysis, may be useful for prospective Biostatisticians looking to gain a strong foundation in the field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex problems in a variety of industries. They work with businesses to improve efficiency and profitability. This course, which covers data cleaning, data visualization, and analysis, may be useful for prospective Operations Research Analysts looking to gain a strong foundation in the field.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work in a variety of industries, including healthcare, finance, and education. This course, which covers data cleaning, data visualization, and analysis, may be useful for prospective Statisticians looking to gain a strong foundation in the field.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations and improve decision-making. This course, which covers data cleaning, data visualization, and analysis, may be useful for prospective Data Analysts looking to gain a strong foundation in the field.
Financial Analyst
Financial Analyst
Financial Analysts collect and analyze financial data to make recommendations about investments. They work with individuals and institutions to help them make informed financial decisions. This course, which covers data cleaning, data visualization, and analysis, may be useful for prospective Financial Analysts looking to gain a strong foundation in the field.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the infrastructure that stores and processes data. They work with data scientists and other stakeholders to ensure that data is accessible and reliable. This course, which covers data cleaning, data visualization, and analysis, may be useful for prospective Data Engineers looking to gain a strong foundation in the field.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. They work with stakeholders to identify needs, develop solutions, and track progress. This course, which covers data cleaning, data visualization, and analysis, may be useful for prospective Business Analysts looking to gain a strong foundation in the field.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to ensure that products meet the needs of users. This course, which covers problem framing and data analysis, may be useful for prospective Product Managers looking to gain a strong foundation in the field.
Data Scientist
Data Scientist
Data Scientists leverage expertise in both business and technology to provide curated solutions to complex data problems. They work across all industries and are hired to gain insights from data, build data-driven products, and improve existing processes. This course, which covers the major stages of a typical ML pipeline, may be useful for prospective Data Scientists looking to advance their knowledge of functional engineering, model training, and evaluation.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with users to understand their needs and develop solutions that meet those needs. This course, which covers functional engineering and model training, may be useful for prospective Software Engineers looking to gain a strong foundation in the field.
For more career information including salaries, visit:
OpenCourser.com/course/xr6n9u/the
Reading list
We've selected 14 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
The Machine Learning Process.
Provides a comprehensive overview of statistical learning, covering both the theoretical foundations and practical applications. It valuable resource for students and practitioners alike.
Save
Provides a comprehensive overview of data mining, covering both the theoretical foundations and practical applications. It valuable resource for students and practitioners alike.
Provides a comprehensive overview of pattern recognition and machine learning, covering both the theoretical foundations and practical applications. It valuable resource for students and practitioners alike.
Provides a comprehensive overview of machine learning algorithms. It covers a wide range of topics, from supervised learning to unsupervised learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for students and practitioners alike.
Save
Provides a comprehensive overview of deep learning, covering both the theoretical foundations and practical applications. It valuable resource for students and practitioners alike.
Provides a comprehensive overview of machine learning, covering both the theoretical foundations and practical applications. It valuable resource for students and practitioners alike.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, from data preparation to model evaluation.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, from data preparation to model evaluation.
Provides a comprehensive overview of machine learning using R. It covers a wide range of topics, from data preparation to model evaluation.
Provides a practical introduction to machine learning for hackers. It covers a wide range of topics, from data preparation to model evaluation.
Provides a practical introduction to machine learning. It covers a wide range of topics, from data preparation to model evaluation.
Provides a practical introduction to machine learning design patterns. It covers a wide range of topics, from data preparation to model evaluation.
Save
Provides a practical introduction to machine learning for business. It covers a wide range of topics, from data preparation to model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xr6n9u/the
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Time
59 hours
Level
Introductory
Via
Pluralsight
Instructor
AWS
Language
English
Good to know
Examines the core stages of a typical ML pipeline, including problem framing, data cleaning, and model training, which is standard in industry
Taught by AWS, who are recognized for their work in cloud computing and ML
Develops functional engineering skills, which are essential for building and deploying ML models
Provides a multi-modal learning experience with videos, readings, and hands-on labs
Requires learners to have a basic understanding of ML concepts
Does not cover advanced ML techniques or algorithms
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