HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.
InstructorLazy Programmer Team
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These 4 complementary views highlight concrete, legible examples from the course presentation.
This slide outlines the course structure for learning Hidden Markov Models, emphasizing theory, implementation from scratch using plain Python, and application to real-world datasets.
This slide outlines the course philosophy, emphasizing learning machine learning from first principles with real programming and math rather than relying on libraries like SciKit-Learn.
This slide presents complex mathematical derivations involving summation formulas and variables like s, y, and epsilon, overlaid with green speech bubbles commenting on the density of the material.
This slide outlines the instructor's professional background, highlighting over ten years of industry experience in full stack development, backend systems, big data processing, and machine learning.
Instruction covers essential HMM algorithms, including the Forward-sBackward and Viterbi methods, as well as continuous observation modeling via Gaussian Mixture Models.
The curriculum includes practical applications of HMMs through code, covering both discrete and continuous observation scenarios.
The curriculum relies on Theano, a library that has largely been superseded in modern deep learning workflows. One signal from 2023 suggests TensorFlow implementations may also be outdated; while the course was updated in 2026, this update does not guarantee these specific code sections were refreshed.
One uncertain signal from 2026 suggests the course structure can feel rambling or lacks a clear design map for certain code sections.
The curriculum aligns with the stated goals of sequence analysis, though some signals suggest a mismatch in instructional approach for all learners.