Hidden Markov Models Courses and Certifications

IBM;  Amazon Microcredential

Natural Language Processing

309 ratings at Udacity

Master advanced NLP techniques, from text cleaning to machine translation. Build models for sentiment analysis, speech recognition, and more using deep learning and neural networks.

• Udacity
• 53 hours
• On-Demand
• Paid Course

Introduction to Natural Language Processing

This section provides an overview of the program and introduces the fundamentals of Natural Language Processing through symbolic manipulation, including text cleaning, normalization, and tokenization. You'll then build a part of speech tagger using hidde…

• Udacity
• 11 hours
• On-Demand
• Paid Course

Fundamentals of Probabilistic Graphical Models

Learn to represent complex probability distributions with Bayes Nets and use Hidden Markov Models for pattern recognition in various fields like computer vision, bioinformatics, and natural language processing.

• Udacity
• 14 hours
• On-Demand
• Paid Course

International Centre for Theoretical Sciences

Sequence Alignment - Hidden Markov Models, Category Theory and All That Jazz by Soumyashant Nayak

Explore sequence alignment methods, from traditional algorithms to modern pseudo-alignment techniques, with insights on hidden Markov models and applications in RNA-Seq data analysis and evolutionary genetics.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

NDC Conferences

Math, Juggling, Hidden Markov Models, and Embedded Python

Explore the intersection of mathematics, juggling, and technology through an abstract model. Discover connections between ancient concepts and modern applications using embedded Python for real-time analysis.

• YouTube
• 40 minutes
• On-Demand
• Conference Talk

Manolis Kellis

Epigenomics and Hidden Markov Models in Computational Biology - Lecture 5

Explore epigenomics, histone modifications, and DNA profiling techniques. Learn about read mapping, quality control, and Hidden Markov Models for analyzing chromatin states in computational biology.

• YouTube
• 1 hour 24 minutes
• On-Demand
• Free Video

UofU Data Science

Natural Language Processing: POS Tagging, Named Entity Recognition, and Hidden Markov Models

Master natural language processing fundamentals through POS tagging, named entity recognition, and hidden Markov models for enhanced text analysis and linguistic structure prediction.

• YouTube
• 1 hour 10 minutes
• On-Demand
• Free Video

Finding Mutations in DNA and Proteins (Bioinformatics VI)

61 ratings at Coursera

Explore advanced DNA and protein mutation analysis using combinatorial pattern matching and hidden Markov models. Learn to map genome fragments and identify protein functions in highly mutated scenarios like HIV research.

• Coursera
• 24 hours
• On-Demand
• Paid Course

Dynamic Programming: Applications In Machine Learning and Genomics

Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution.

• edX
• 4 weeks, 8-10 hours a week
• On-Demand
• Free Online Course (Audit)

Speech Recognition with Python

153 ratings at Udemy

Master Speech Recognition with Python: From Fundamentals to Cutting-Edge AI Applications

• Udemy
• 3 hours 25 minutes
• On-Demand
• Paid Course

NLP in Python: Probability Models, Statistics, Text Analysis

54 ratings at Udemy

Master Language Models, Hidden Markov Models, Bayesian Methods & Sentiment Analysis for Real-World Applications

• Udemy
• 6 hours 24 minutes
• On-Demand
• Paid Course

Applied Natural Language Processing in Engineering Part 2

Master advanced NLP techniques including RNNs, transformers, sentiment analysis, and neural machine translation through hands-on implementation with PyTorch and Hugging Face frameworks.

• Coursera
• 1 day 9 hours 37 minutes
• On-Demand
• Paid Course

Fundamentals of Dynamic Programming

388 ratings at LinkedIn Learning

Dynamic programming makes it possible to solve challenging problems efficiently. Learn what it is, how and when to apply it to your algorithms, and how it's used in applications.

• LinkedIn Learning
• 1 hour 26 minutes
• On-Demand
• Free Trial Available

Bayesian Networks 2 - Forward-Backward - Stanford CS221: AI

Explore advanced probabilistic inference techniques, including hidden Markov models, particle filtering, and Gibbs sampling, for AI applications in object tracking and more.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Online Course

Machine Learning and Knowledge Discovery

Master machine learning fundamentals through hands-on PyTorch implementation, covering neural networks, SVMs, deep learning architectures, and practical applications in robotics, computer vision, and text analysis.

• XuetangX
• On-Demand
• Free Online Course