AWS Certified Machine Learning: Problem Framing & Algorithm Selection

Amazon Web Services    |    Intermediate
  • 12 videos | 1h 7m 30s
  • Includes Assessment
  • Earns a Badge
Rating 4.6 of 20 users Rating 4.6 of 20 users (20)
Problem framing and algorithm selection is the most important part of any machine learning (ML) project. ML engineers have to apply appropriate techniques that will result in expected prediction behavior. It is important to fully understand a particular task and choose among all the available methods and toolkits before implementing a machine learning project. Use this course to learn more about the ML mindset, discover how goal-oriented business problems can be formulated as machine learning problems, and describe factors that drive the selection of the correct algorithm for a particular scenario. The course will also help you refresh important ML concepts and terminologies. After completing this course, you'll be able to implement machine learning solutions to solve business problems, further preparing you for the AWS Certified Machine Learning - Specialty certification exam.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline machine learning (ml) mindset and compare the ml approach to other problem-solving techniques
    Define the key characteristics of good machine learning problems
    Describe the most challenging problems in machine learning (ml)
    Specify how to clearly define a business problem and set success and failure criteria
    Describe how to design a good output for a business problem
  • Identify how to formulate a business problem into a machine learning problem
    Define the importance of the availability of good data and data pipeline design
    Evaluate the learning ability of a machine learning model and identify potential risks and biases in the dataset as well as their resulting impact
    Specify the factors that impact algorithm selection for a particular use case
    Review core machine learning concepts covered in the aws examination, such as confusion matrices, precision, and recall
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 41s
  • 5m 54s
    During this video, you will discover how to outline the machine learning (ML) mindset and compare the ML approach to other problem-solving techniques. FREE ACCESS
  • Locked
    3.  Machine Learning (ML) Solvable Problems
    9m 24s
    In this video, you will learn how to define the key characteristics of good machine learning problems. FREE ACCESS
  • Locked
    4.  Difficult Problems in Machine Learning
    4m 52s
    Discover how to describe the most challenging problems in machine learning. FREE ACCESS
  • Locked
    5.  Identifying Machine Learning Use Cases and Metrics
    5m 44s
    In this video, you will learn how to clearly define a business problem and set success and failure criteria. FREE ACCESS
  • Locked
    6.  Identifying Expected Outcome
    3m 53s
    After completing this video, you will be able to describe how to design a good output for a business problem. FREE ACCESS
  • Locked
    7.  Formulating Machine Learning Questions
    5m 59s
    In this video, find out how to identify and formulate a business problem into a machine learning problem. FREE ACCESS
  • Locked
    8.  Data Sources and Data Preparation for ML
    5m 59s
    Upon completion of this video, you will be able to define the importance of the availability of good data and data pipeline design. FREE ACCESS
  • Locked
    9.  Learning Ability and Potential Bias
    7m 27s
    During this video, you will learn how to evaluate the learning ability of a machine learning model, identify potential risks and biases in the dataset, and understand their resulting impact. FREE ACCESS
  • Locked
    10.  Considerations for Algorithm Selection
    6m 21s
    Find out how to specify the factors that impact algorithm selection for a particular use case. FREE ACCESS
  • Locked
    11.  Machine Learning Refresher
    8m 11s
    Learn how to review core machine learning concepts covered in the AWS examination, such as confusion matrices, precision, and recall. FREE ACCESS
  • Locked
    12.  Course Summary
    1m 6s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course Low-code ML with KNIME: Getting Started with the KNIME Analytics Platform
Rating 4.6 of 9 users Rating 4.6 of 9 users (9)
Course AWS Certified Machine Learning: Feature Engineering Techniques
Rating 3.9 of 19 users Rating 3.9 of 19 users (19)
Journey MLOps
Rating 4.7 of 6 users Rating 4.7 of 6 users (6)

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course AWS Certified Machine Learning: Data Engineering, Machine Learning, & AWS
Rating 4.3 of 152 users Rating 4.3 of 152 users (152)
Course AWS DevOps Engineer Professional 2021: Deployment Strategies
Rating 4.8 of 6 users Rating 4.8 of 6 users (6)
Course Fundamentals of AI & ML: Foundational Data Science Methods
Rating 4.4 of 294 users Rating 4.4 of 294 users (294)