Unsupervised Machine Learning
This course ranks f10th in the top best online Machine Learning courses. This course will introduce you to unsupervised learning, which is one of the most common forms of machine learning. You'll discover how to extract information from data sets that lack a target or labeled variable. You'll learn how to choose the optimal method for your data from a variety of clustering and dimension reduction strategies for unsupervised learning. This course's hands-on component focuses on how to employ best practices for unsupervised learning.
- Explain the types of issues that are suited for Unsupervised Learning techniques before the conclusion of this course.
- Explain how the curse of dimensionality makes clustering with numerous characteristics difficult.
- Explain and implement standard clustering and dimensionality reduction techniques.
- When appropriate, try grouping points and comparing the performance of per-cluster models.
- Recognize key metrics for cluster classification.
This course is designed for aspiring data scientists who want to learn how to use Unsupervised Machine Learning techniques in a corporate context. You should be familiar with Python programming and have a basic grasp of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics in order to get the most out of this course.
Syllabus:
- WEEK1 (3 hours to complete): Introduction to Unsupervised Learning and K Means
- WEEK 2 (5 hours to complete): Selecting a clustering algorithm
- WEEK 3 (5 hours to complete): Dimensionality Reduction
Provider: Coursera
Cost: Premium
Rate:4.7/5
Enroll here: https://tinyurl.com/52rra8wz
