Fundamentals of AI & ML: Foundational Data Science Methods

Artificial Intelligence    |    Intermediate
  • 12 videos | 44m 53s
  • Includes Assessment
  • Earns a Badge
  • Certification PMI PDU
Rating 4.4 of 294 users Rating 4.4 of 294 users (294)
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. In this course, you will examine what machine learning is, how it is categorized, and some everyday use cases for supervised and unsupervised machine learning. Then you will discover feature engineering and its impact on model performance. Next, focus on common types of machine learning tasks, such as clustering, classification, and simple and multiple linear regression. Finally, explore various machine learning challenges and how to overcome them. Upon completion, you will 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 the process of feature engineering and its impact on model performance
    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

  • 43s
  • 8m 58s
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    3.  Feature Engineering
    5m 25s
    In this video, we will outline the process of feature engineering and its impact on model performance. FREE ACCESS
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    4.  Clustering
    4m 4s
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    5.  Evaluation of Clustering Algorithm Accuracy
    4m 51s
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    6.  Classification
    4m 42s
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    7.  Evaluation of Classification Model Accuracy
    3m 4s
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    8.  Regression
    1m 47s
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    9.  Simple Linear Regression
    4m 15s
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    10.  Multiple Linear Regression
    2m 5s
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    11.  Machine Learning Challenges
    4m 26s
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    12.  Course Summary
    33s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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