Description
Unsupervised Machine Learning: Hidden Markov Models in Python
Hidden Markov Models (HMMs) power speech recognition, DNA sequencing, and NLP—but most tutorials skip the math and code. This course dives deep into **forward-backward, Viterbi, and Baum-Welch algorithms**, with hands-on implementations in Python for real-world sequence modeling problems.
What You’ll Build
- A speech recognizer that maps audio features to phonemes
- A POS tagger that labels parts of speech in text
- A stock regime detector that identifies market states (bull/bear/volatile)
- A DNA sequence analyzer for gene prediction in bioinformatics
Key Concepts Covered
- Markov chains—memoryless stochastic processes
- Hidden states vs observations—the core HMM distinction
- Forward-Backward algorithm—compute likelihood of observations
- Viterbi algorithm—find the most likely hidden state sequence
- Baum-Welch (EM)—learn HMM parameters from unlabeled data
Why HMMs in 2025?
- Fundamental in sequence modeling—still used in finance, bio, and legacy NLP systems
- Great for small-data regimes—unlike deep learning, HMMs work with limited sequences
- Interview favorite—tested in ML roles at finance and biotech firms
Who Should Take This?
- ML engineers working with time-series or sequential data
- Bioinformatics researchers analyzing DNA/protein sequences
- Speech or NLP practitioners needing classical baselines
- Students preparing for advanced ML interviews
Go Beyond Deep Learning—Master the Classics
HMMs may be “old school,” but they’re **interpretable, efficient, and effective**—perfect for domains where data is scarce or explainability matters.
Ready to model the hidden states of the world? Enroll now.
