Using BigML: An Introduction to Machine Learning & BigML

Machine Learning 2020    |    Beginner
  • 11 videos | 1h 10m 42s
  • Includes Assessment
  • Earns a Badge
From self-driving cars to predicting stock prices, machine learning has an exciting range of applications. BigML, due to its ease of use, makes these algorithms widely accessible. This course outlines machine learning fundamentals and how these are applied in BigML. You'll start by examining various machine learning algorithm categories and the kinds of problems they're used to solve. You'll then investigate the classification problem and the process involved in training and evaluating such models. Next, you'll examine linear regression and how this can help predict a continuous value. Moving on, you'll explore the concept of unsupervised learning and its application in clustering, Principal Component Analysis (PCA), and generating associations. Finally, you'll recognize how all of this comes together when using BigML to significantly simplify the building and maintenance of your machine learning models.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Recognize machine learning algorithm types and their applications
    Describe the process of using training data to construct a machine learning model
    Recall the various metrics used to evaluate the quality of a machine learning model
    Describe the features and use cases of linear regression
    Distinguish between supervised and unsupervised learning algorithms
  • Recognize the purpose of clustering algorithms and list some of their use cases
    Describe the factors involved in extracting principal components from large datasets
    Recognize what association rules are and state their applications
    List the features of bigml and the variety of models that can be built using this tool
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 34s
  • 7m 11s
    After completing this video, you will be able to recognize different types of machine learning algorithms and their applications. FREE ACCESS
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    3.  How Machine Learning Works
    7m 40s
    Upon completion of this video, you will be able to describe the process of using training data to construct a machine learning model. FREE ACCESS
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    4.  Confusion Matrix
    7m 6s
    After completing this video, you will be able to recall the various metrics used to evaluate the quality of a machine learning model. FREE ACCESS
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    5.  Linear Regression
    9m 19s
    After completing this video, you will be able to describe the features and use cases of linear regression. FREE ACCESS
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    6.  Supervised vs. Unsupervised Learning
    8m 7s
    During this video, you will learn how to distinguish between supervised and unsupervised learning algorithms. FREE ACCESS
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    7.  Clustering in ML
    6m 25s
    Upon completion of this video, you will be able to recognize the purpose of clustering algorithms and list some of their use cases. FREE ACCESS
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    8.  Principal Component Analysis (PCA)
    7m 13s
    Upon completion of this video, you will be able to describe the factors involved in extracting principal components from large datasets. FREE ACCESS
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    9.  Associations in ML
    8m 18s
    After completing this video, you will be able to recognize what association rules are and state their applications. FREE ACCESS
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    10.  BigML and Machine Learning
    5m 17s
    Upon completion of this video, you will be able to list the features of BigML and the variety of models that can be built using this tool. FREE ACCESS
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    11.  Course Summary
    1m 31s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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