Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
6 Hour(s) 24 Minute(s)
Language
English
Taught by
Meta Brains, Skool of AI
Rating
4 views
Course Overview
Master Language Models, Hidden Markov Models, Bayesian Methods & Sentiment Analysis for Real-World Applications
Unlock the power of Natural Language Processing (NLP) with this comprehensive, hands-on course that focuses on probability-based approaches using Python. Whether you're a data scientist, software engineer, or ML enthusiast, this course will transform you from a beginner to a confident NLP practitioner through practical, real-world projects and exercises.
Starting with fundamental text processing techniques, you'll progressively master advanced concepts like Hidden Markov Models, Probabilistic Context-Free Grammars, and Bayesian Methods. Unlike other courses that only scratch the surface, we dive deep into the probabilistic foundations that power modern NLP applications while keeping the content accessible and practical.
What sets this course apart is its project-based approach. You'll build:
A complete text preprocessing pipeline
Custom language models using N-grams
Part-of-speech taggers with Hidden Markov Models
Sentiment analysis systems for e-commerce reviews
Named Entity Recognition models using probabilistic approaches
Through carefully designed mini-projects in each section and a comprehensive capstone project, you'll gain hands-on experience with essential NLP libraries and frameworks. You'll learn to implement various probability models, from basic Naive Bayes classifiers to advanced topic modeling with Latent Dirichlet Allocation.
By the end of this course, you'll have a robust portfolio of NLP projects and the confidence to tackle real-world text analysis challenges. You'll understand not just how to use popular NLP tools, but also the probabilistic principles behind them, giving you the foundation to adapt to new developments in this rapidly evolving field.
Whether you're looking to enhance your career prospects in data science, improve your organization's text analysis capabilities, or simply understand the mathematics behind modern NLP systems, this course provides the perfect balance of theory and practical implementation
Course Content
- 10 section(s)
- 56 lecture(s)
- Section 1 Introduction to Natural Language Processing (NLP)
- Section 2 Probability Theory and Statistics for NLP
- Section 3 Feature Extraction Techniques
- Section 4 Language Modeling and N-grams
- Section 5 Hidden Markov Models (HMM)
- Section 6 Probabilistic Context-Free Grammars (PCFG)
- Section 7 Bayesian Methods in NLP
- Section 8 Mid-Course Project: Combining Techniques
- Section 9 Sentiment Analysis and Named Entity Recognition
- Section 10 Advanced Topics in Probabilistic NLP
What You’ll Learn
- Design and deploy a complete sentiment analysis pipeline for analyzing customer reviews, combining rule-based and machine learning approaches
- Master text preprocessing techniques and feature extraction methods including TF-IDF, Word Embeddings, and implement custom text classification systems
- Develop production-ready Named Entity Recognition systems using probabilistic approaches and integrate them with modern NLP libraries like spaCy
- Create and train sophisticated language models using Bayesian methods, including Naive Bayes classifiers and Bayesian Networks for text analysis
- Build a comprehensive e-commerce review analysis system that combines sentiment analysis, entity recognition, and topic modeling in a real-world application
- Build and implement probability-based Natural Language Processing models from scratch using Python, including N-grams, Hidden Markov Models, and PCFGs
Reviews
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DDann Lineses
It was a good match to me. It had exactly what I needed to learn
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GGurukrushna santara
seems good but the notes shown if shared will be better
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MMajed Al Nowab
The course videos needs to be updated
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VVAIBHAV PANCHAL
It is really amazing to have you as NLP guide.
