Unsupervised Machine Learning Hidden Markov Models in Python
HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.
Tailored for data scientists, analysts, and enthusiasts, this program offers a deep dive into the principles and applications of Hidden Markov Models, empowering learners to uncover patterns and structure within sequential data.
What You Will Learn:
- Introduction to Hidden Markov Models (HMM):
- Gain a fundamental understanding of Hidden Markov Models and their applications in unsupervised machine learning.
- Explore the underlying principles of state transitions and observable outcomes in sequential data.
- Probability and Transition Matrices:
- Dive into probability theory and transition matrices, foundational concepts for understanding the dynamics of Hidden Markov Models.
- Learn how to construct and interpret transition matrices for different states.
- Emission Probabilities and Observations:
- Understand emission probabilities and their role in generating observable outcomes.
- Explore how observations are generated based on the underlying state of the Hidden Markov Model.
- Learning Parameters from Data:
- Learn techniques for estimating model parameters from observed sequential data.
- Understand the expectation-maximization (EM) algorithm for training Hidden Markov Models.
- Decoding and State Inference:
- Explore methods for decoding and inferring the hidden states from observed sequences.
- Understand the Viterbi algorithm for finding the most likely sequence of hidden states.
- Applications in Time Series Analysis:
- Apply Hidden Markov Models to time series data for anomaly detection, prediction, and pattern recognition.
- Explore real-world examples and case studies showcasing the versatility of HMMs.
- Speech Recognition and Natural Language Processing:
- Delve into applications of Hidden Markov Models in speech recognition and natural language processing.
- Understand how HMMs model sequential data in language and speech-related tasks.
- Real-world Projects and Case Studies:
- Apply your knowledge through hands-on projects and real-world case studies.
- Gain practical experience in implementing Hidden Markov Models for various applications.
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