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All Classes Will Be Held Virtually – Live Online Intertech's Training Division has been successfully instructing professionals through virtual live online training since the advent of the smartboard. It is a proven form and offers the convenience of live questions, group interaction, and labs with an instructor looking over your shoulder. Because of this, we will continue all classes live but virtually, including Agile and Scrum instruction, so businesses and individual’s seeking professional development can keep moving forward during these unexpected times.

Machine Learning for Application Developers Training

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Bring This Course To You

For groups of 5 or more, let Intertech bring this course to your location. Customized versions tailored towards your objectives are also available.

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Learning Objectives

  • Learn to translate business problems into machine learning algorithms.
  • Know how to treat unstructured and semi structured data, such as text, time series, spatial, graph data, and images.
  • Understand the generalization error of the model before deployment
  • Ensure proper handling of training and testing data so the testing data mimics incoming data when the model is deployed in production
  • Selecting the appropriate objective/loss function inspired by the business value is important for ultimate success in the application
  • Understanding features in the data and improving upon them (by creating new features and eliminating existing ones) has a high impact in terms of predictability
  • Selecting the machine learning method that works best for the given problem is key and often determines success or failure

Course Outline

Lesson 1: Python Ecosystem for Machine Learning. 

Lesson 2: Python and SciPy Crash Course. 

Lesson 3: Load Datasets from CSV. 

Lesson 4: Understand Data With Descriptive Statistics. 

Lesson 5: Understand Data With Visualization. 

Lesson 6: Pre-Process Data. 

Lesson 7: Feature Selection. 

Lesson 8: Resampling Methods. 

Lesson 9: Algorithm Evaluation Metrics. 

Lesson 10: Spot-Check Classification Algorithms. 

Lesson 11: Spot-Check Regression Algorithms. 

Lesson 12: Model Selection. 

Lesson 13: Pipelines. 

Lesson 14: Ensemble Methods. 

Lesson 16: Model Finalization. 

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