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Machine Learning using Python Deep Dive

In this Python for ML training course, attendees take a deep dive into machine learning, including supervised and unsupervised learning, regression, classification, and clustering. Students also...

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$2,995 USD
Course Code WA3375
Duration 5 days
Available Formats Classroom, Virtual

In this Python for ML training course, attendees take a deep dive into machine learning, including supervised and unsupervised learning, regression, classification, and clustering. Students also learn how to implement ML algorithms in Python, a popular programming language for machine learning.

Skills Gained

  • Understand machine learning as a useful tool for predictive models
  • Know when to reach for machine learning as a tool
  • Implement data preprocessing for an ML workflow
  • Understand the difference between supervised and unsupervised tasks
  • Implement several classification algorithms
  • Evaluate model performance using a variety of metrics
  • Compare models across a workflow
  • Implement regression algorithm variations
  • Understand clustering approaches to data
  • Interpret labels generated from clustering
  • Transform unstructured text data into structured data
  • Understand text-specific data preparation
  • Visualize frequency data from text sources
  • Perform topic modeling on a collection of documents
  • Use labeled text to perform document classification

Prerequisites

All attendees should have completed the Comprehensive Data Science with Python class or have equivalent experience.

Course Details

Course Outline

  • Introduction
  • An Overview of Machine Learning
  • Modeling for explanation (descriptive models)
  • Supervised Learning: Regression
  • Supervised Learning: Classification
  • Unsupervised Learning: Clustering
  • Clustering for Treatment Effect Heterogeneity
  • Data Munging and Machine Learning Via H20
  • Introduction to Natural Language Processing (NLP)
  • NLP Normalization, Parts-of-speech and Topic Modeling
  • NLP and Machine Learning
  • Conclusion
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