In this course, you will develop your data science skills while solving real-world problems. You'll work through the data science process to and use unsupervised learning to explore data, engineer and select meaningful features, and solve complex supervised learning problems using tree-based models. You will also learn to apply hyperparameter tuning and cross-validation strategies to improve model performance.
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NOTE: This is the third and final course in the Data Science with Databricks for Data Analysts Coursera specialization. To be successful in this course we highly recommend taking the first two courses in that specialization prior to taking this course. These courses are: Apache Spark for Data Analysts and Data Science Fundamentals for Data Analysts.
What You Will Learn
- Explore data using unsupervised machine learning.
- Solve complex supervised learning problems using tree-based models.
- Apply hyperparameter tuning and cross-validation strategies to improve model performance.
Course 3 of 3 in the Data Science with Databricks for Data Analysts Specialization.
Syllabus
WEEK 1: Welcome to the Course
WEEK 2: Applied Unsupervised Learning
WEEK 3: Feature Engineering and Selection
WEEK 4: Applied Tree-based Models
WEEK 5: Model Optimization
