We may earn an affiliate commission when you visit our partners.

The Machine Learning Process

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.

This course is no longer available. Find something similar by browsing:

Machine Learning Pipeline Data Cleaning Data Visualization Functional Engineering Model Training Model Evaluation Amazon ML Pipeline

What's inside

Syllabus

Traffic lights

Read about what's good

what should give you pause

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

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Ml process: comprehensive overview for beginners

According to learners, this course provides an excellent overview of the entire Machine Learning process, from problem framing to model evaluation. Students consistently praise its ability to demystify the ML pipeline, offering a solid conceptual foundation. The inclusion of real-world examples from Amazon is frequently highlighted as a significant positive, making concepts tangible. While it serves as a great starting point for beginners and is ideal for managers or product owners seeking a high-level understanding, some reviewers note its limited technical depth and wish for more hands-on coding exercises. Overall, it is highly recommended for understanding the big picture, less so for in-depth implementation.

Complex topics explained simply and clearly.

"The instructor explains complex topics clearly."

"The instructor is clear and easy to follow."

"The instructor is knowledgeable and presents the material well."

Perfect for new learners or non-technical roles.

"It provides a solid foundation for anyone looking to get into ML operations or just understand the lifecycle better."

"This course demystifies the entire ML lifecycle. It's perfect for managers or product owners who need to understand what their ML teams are doing."

"As an aspiring data scientist, I found it useful for getting a solid grasp of the big picture."

Real-world case studies enhance understanding of concepts.

"I especially appreciated the real-world examples from Amazon."

"The examples from Amazon truly bring the concepts to life."

"The Amazon case studies added practical relevance, making the concepts tangible."

Provides a strong, high-level understanding of the ML pipeline.

"This course is an excellent overview of the entire ML process."

"It effectively covers each stage from beginning to end, providing a comprehensive, high-level understanding."

"I now feel much more confident in approaching ML projects from an end-to-end perspective."

More theoretical, could benefit from practical labs.

"My main feedback would be to include more coding exercises or hands-on labs. It's quite theoretical in parts..."

"I wish there were more hands-on labs, but as an introduction, it serves its purpose well."

"It felt like a long presentation rather than an interactive learning experience."

More of a high-level overview than a deep technical dive.

"I felt some parts were a bit superficial and could have gone deeper, especially on model evaluation metrics."

"For someone with a bit of experience, it feels very basic. I was hoping for more technical depth."

"If you already know what ML is, you probably won't learn much new. It barely scratches the surface of important topics."

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.

Career center

Learners who complete The Machine Learning Process will develop knowledge and skills that may be useful to these careers:

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Biostatistician

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.

See salaries and explore the career path for Epidemiologist

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.

See salaries and explore the career path for Market Researcher

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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Quantitative 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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Data Scientist

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.

The Elements of Statistical Learning

Save

Provides a comprehensive overview of statistical learning, covering both the theoretical foundations and practical applications. It valuable resource for students and practitioners alike.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Kindle Edition

$$$

Data Mining

Save

Provides a comprehensive overview of data mining, covering both the theoretical foundations and practical applications. It valuable resource for students and practitioners alike.

The Machine Learning Process

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Ml process: comprehensive overview for beginners

Activities

Career center

Reading list

Share

Similar courses