This course provides practical techniques for applying data science methods. Build upon foundational knowledge to tackle complex data challenges, covering data preparation, feature engineering, model building, evaluation, and deployment fundamentals. Designed for those familiar with basic data science concepts and Python.
Prerequisites Basic Python & DS Concepts
Level:
Advanced Data Preparation
Implement sophisticated techniques for cleaning, transforming, and integrating diverse datasets.
Feature Engineering
Create and select informative features from raw data to improve model performance.
Model Building and Tuning
Apply and fine-tune various supervised and unsupervised learning algorithms.
Model Evaluation
Utilize appropriate metrics and validation strategies to assess and compare model performance.
Deployment Fundamentals
Understand the basics of preparing and deploying machine learning models using APIs and containers.
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