Fundamentals of AI & ML: Foundational Data Science Methods

Artificial Intelligence    |    Intermediate
  • 11 videos | 33m 44s
  • Includes Assessment
  • Earns a Badge
Rating 4.4 of 286 users Rating 4.4 of 286 users (286)
Data science methods are used across several industries to deliver value to businesses. Machine learning (ML) is a data science method that uses prediction algorithms that find patterns in massive amounts of data, allowing machines to predict future results and make decisions with minimal human intervention. Through this course, learn foundational methods for using machine learning. Examine what machine learning is, how it is categorized, and common machine learning challenges. Next, learn about common types of machine learning tasks, such as clustering, classification, and regression. Finally, explore the types of regression, including simple and multiple linear regression. Upon completion, you'll be able to define machine learning and methods for using it.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Identify use cases for machine learning and differentiate supervised and unsupervised machine learning
    Outline clustering and differentiate its benefits and challenges
    List considerations for evaluating the accuracy of a clustering algorithm
    Identify the uses of classification and name common classifiers
    List considerations for evaluating the accuracy of a classification model
  • Outline regression, its benefits, and challenges
    Name use cases for simple linear regression
    Outline how to use multiple linear regression
    List common machine learning challenges
    Summarize the key concepts covered in this course

IN THIS COURSE

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

Rating 4.4 of 521 users Rating 4.4 of 521 users (521)
Rating 4.6 of 21 users Rating 4.6 of 21 users (21)
Rating 4.5 of 264 users Rating 4.5 of 264 users (264)