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Machine Learning for Data Scientists
Course Description
Overview
Objectives
Audience
Prerequisites
- Attendants should have knowledge:
- Python
- Spark fundamentals
- Pyspark fundamentals
Topics
- Unit 1. Data Science Overview
- Unit 2. Introduction to Artificial Intelligence and Machine Learning
- Unit 3. Pyspark SparkML
- Unit 4. Feature Engineering
- Unit 5. Advanced Feature Engineering
- Unit 6. Machine learning steps and functions
- Unit 7. Supervised Learning – Regression
- Unit 8. Supervised Learning – Classification
- Unit 9. Unsupervised Learning – Clustering
- Unit 10. Deep Learning
- Unit 11. Data Pipelines in Spark
- Unit 12. Machine Learning model management
- Unit 13. Introduction to GraphFrames and Graph Processing
- Unit 14. Machine Learning and GraphFrames
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Self-Paced Training Info
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- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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