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

Data Science with Databricks for Data Analysts Specialization

Unsupervised Learning

Coursera Plus

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MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

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