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Janani Ravi and Mohammed Osman

Machine Learning is the application of algorithms and mathematical models by software system to progressively improve their performance on a specific task. This skill covers the workflows, modeling techniques, and strategies behind any machine learning solution.

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Machine Learning is the application of algorithms and mathematical models by software system to progressively improve their performance on a specific task. This skill covers the workflows, modeling techniques, and strategies behind any machine learning solution.

What You'll Learn

  • The machine learning workflow (data sourcing -> data cleaning -> data preparing -> data modeling and training -> model evaluation -> deployment -> monitoring & maintenance )
  • Commonly employed data models
  • Common techniques employed in machine learning (reinforcement learning, model validation strategies, etc)
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    What's inside

    Four courses

    Preparing Data for Machine Learning

    (3 hours)
    This course covers data preparation, cleaning, and feature selection for machine learning models. You will learn to handle missing data, identify and cope with outliers, and select the most relevant features for your model.

    Designing a Machine Learning Model

    (3 hours)
    This course covers the key differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.

    Creating Machine Learning Models

    (2 hours)
    This course covers the important types of machine learning algorithms, solution techniques based on the specifics of the problem you are trying to solve, as well as the classic machine learning workflow.

    Deploying Machine Learning Solutions

    (3 hours)
    This course covers why models underperform post-deployment and how to mitigate this. You will learn about training-serving skew, concept drift, and overfitting. You will also discover how to deploy models using Flask, serverless environments, and platform-specific machine learning services.

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