You have a complex problem involving a large amount of data and lots of variables. You know that machine learning would be the best approach—but you’ve never used it before. How do you deal with data that’s messy, incomplete, or in a variety of formats? How do you choose the right model for the data?

Sounds daunting? Don’t be discouraged. A systematic workflow will help you get off to a smooth start.

Read the ebook to go step by step from the basics to advanced techniques and algorithms:

  • Section 1: Introducing Machine Learning
    Learn the basics of machine learning, including supervised and unsupervised learning, choosing the right algorithm, and practical examples.
  • Section 2: Getting Started with Machine Learning
    Step through the machine learning workflow using a health monitoring app as an example. The section covers accessing and loading data, preprocessing data, deriving features, and training and refining models.
  • Section 3: Applying Unsupervised Learning
    Explore hard and soft clustering algorithms, and learn about common dimensionality-reduction techniques for improving model performance.
  • Section 4: Applying Supervised Learning
    Explore classification and regression algorithms, and learn about techniques for model improvement, including feature selection, feature transformation, and hyperparameter tuning.

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Apply these concepts and run example code with a free trial of Statistics and Machine Learning Toolbox™.