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Machine Learning In Action (Part II): Applications for Everyone

Class Sessions

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Class Code

ITS-1902

Class Description

Learn about Machine Learning patterns and use cases in the real world, while getting a review of statistics and data analytics to be more data-minded, helping to understand the Data Science process.

After this course, you will be able to:

  • Describe Supervised and Unsupervised learning techniques and usages
  • Understand techniques like Classification, Clustering and Regression
  • Discuss how to identify which kinds of technique to be applied for specific use case
  • Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python and R etc.
  • Install and set up Anaconda.
  • Perform hands-on activity using Jupyter Notebooks.

 

Topic Outline:

  • Course Introduction
  • Machine Learning patterns
    - Classification
    - Clustering
    - Regression
  • Gartner Hype Cycle for Emerging Technologies
  • Machine Learning offerings in Industry
  • Hands-on exercise 1: Install and Setup Anaconda.
  • Python Libraries
    - NumPy
    - Pandas
    - Scikit Learn
  • Hands-on exercise 2: Data Analysis using Pandas
  • Algorithms
    - Linear Regression
    - Decision Tree
  • Hands-on exercise 3: Perform Linear regression using Scikit-learn
  • References and Next steps

     

 

University IT Technology Training classes are only available to Stanford University staff, faculty, or students. A valid SUNet ID is needed in order to enroll in a class.