When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
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
