Using BigML: An Introduction to Machine Learning & BigML

Machine Learning 2020    |    Beginner
  • 11 videos | 1h 10m 42s
  • Includes Assessment
  • Earns a Badge
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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

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