Excel in Unsupervised Machine Learning Exams: Practice, Master, Succeed!
Sub Category
- Test Prep
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Objectives
- Introduction to Unsupervised Learning
- Understanding Clustering Techniques
- Overview of Markov Chains
- K-means Clustering
- Hierarchical Clustering
- Hidden Markov Models
- Principal Component Analysis (PCA)
- Pattern Recognition
- Gaussian Mixture Models (GMM)
- Expectation-Maximization (EM) Algorithm
- Variational Inference in Hidden Markov Models
- Probability Distributions in Unsupervised Learning
- Mathematical Foundations of Markov Chains
- Dimensionality Reduction Techniques and Theories
Pre Requisites
- Basic understanding of machine learning concepts.
- Familiarity with fundamental algorithms and techniques used in machine learning.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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