NLP - Machine Learning Online Course
This course is a must-have for technical professionals aiming to master the fast-growing domain of natural language processing (NLP).
You’ll begin with a foundational overview of NLP concepts before progressing through structured sections, each centered around real-world problems. Core algorithms such as Naive Bayes, Logistic Regression, and Latent Dirichlet Allocation (LDA) are introduced with intuitive explanations to build a strong conceptual understanding.
Hands-on exercises and practical projects in Python will help you solidify your skills and apply machine learning techniques to real text data.
By the end of the course, you’ll have a solid grasp of key NLP methodologies and the practical experience needed to confidently apply them in real-world applications.
Course Curriculum
Welcome
- Introduction and Outline
- Special Offer
Getting Set Up
- Where To Get the Code
- How To Succeed in This Course
Spam Detection
- Spam Detection - Problem Description
- Naive Bayes Intuition
- Spam Detection - Exercise Prompt
- Aside: Class Imbalance, ROC, AUC, and F1 Score (pt 1)
- Aside: Class Imbalance, ROC, AUC, and F1 Score (pt 2)
- Spam Detection in Python
Sentiment Analysis
- Sentiment Analysis - Problem Description
- Logistic Regression Intuition (pt 1)
- Multiclass Logistic Regression (pt 2)
- Logistic Regression Training and Interpretation
- Sentiment Analysis - Exercise Prompt
- Sentiment Analysis in Python (pt 1)
- Sentiment Analysis in Python (pt 2)
Text Summarization
- Text Summarization Section Introduction
- Text Summarization Using Vectors
- Text Summarization Exercise Prompt
- Text Summarization in Python
- TextRank Intuition
- TextRank - How It Really Works (Advanced)
- TextRank Exercise Prompt (Advanced)
- TextRank in Python (Advanced)
- Text Summarization in Python - The Easy Way (Beginner)
- Text Summarization Section Summary
Topic Modeling
- Topic Modeling Section Introduction
- Latent Dirichlet Allocation (LDA) - Essentials
- LDA - Code Preparation
- LDA - Maybe Useful Picture (Optional)
- Latent Dirichlet Allocation (LDA) - Intuition (Advanced)
- Topic Modeling with Latent Dirichlet Allocation (LDA) in Python
- Non-Negative Matrix Factorization (NMF) Intuition
- Topic Modeling with Non-Negative Matrix Factorization (NMF) in Python
- Topic Modeling Section Summary
Latent Semantic Analysis (Latent Semantic Indexing)
- LSA / LSI Section Introduction
- SVD (Singular Value Decomposition) Intuition
- LSA / LSI: Applying SVD to NLP
- Latent Semantic Analysis / Latent Semantic Indexing in Python
- LSA / LSI Exercises