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.
This course is part of the Data Science with Databricks for Data Analysts Specialization
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About this Course
Completion of the first two courses in the Data Science with Databricks for Data Analysts Coursera specialization. This course is the final course.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat 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.
Skills you will gain
- Data Science
- Machine Learning
- Databricks
Completion of the first two courses in the Data Science with Databricks for Data Analysts Coursera specialization. This course is the final course.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Welcome to the Course
Applied Unsupervised Learning
Feature Engineering and Selection
Applied Tree-based Models
Reviews
- 5 stars72.22%
- 4 stars13.88%
- 2 stars8.33%
- 1 star5.55%
TOP REVIEWS FROM APPLIED DATA SCIENCE FOR DATA ANALYSTS
Very practical applied machine learning course on data science while covering key components informatively
Great course for an overview but with a high level of abstraction (usage of existing libraries but very little coding of algorithms that show the details of the principles)
Well defined exercice with usage of Pyspark in Databricks
About the Data Science with Databricks for Data Analysts Specialization

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