NLP – Machine Learning Models in Python
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NLP – Machine Learning Models in Python
This course is part of Applied NLP and Generative AI Specialization
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What you'll learn
Build and evaluate spam detection models using Naive Bayes and performance metrics.
Implement sentiment analysis with logistic regression in Python.
Create extractive summaries using vector methods and TextRank algorithms.
Apply LDA, NMF, and LSA techniques for uncovering latent topics in text data.
Skills you'll gain
Tools you'll learn
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There are 7 modules in this course
Updated in May 2025.
This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Unlock the power of natural language processing (NLP) with machine learning techniques using Python in this hands-on, application-focused course. You'll gain practical skills in text classification, sentiment analysis, summarization, and topic modeling—all essential tools in the NLP toolkit. By the end of the course, you'll not only understand key algorithms but also be able to implement them confidently in Python. The course begins with setup instructions and success tips to ensure a smooth learning experience. You'll dive into spam detection using Naive Bayes, addressing real-world problems like class imbalance and model evaluation with ROC, AUC, and F1 Score metrics. With guided exercises and code demonstrations, you'll learn to build functional spam filters. Next, you'll explore sentiment analysis through logistic regression, mastering both binary and multiclass classification. Then, you’ll move into text summarization—starting with vector-based approaches and progressing to advanced techniques like TextRank. Both beginner and advanced methods are covered, ensuring an inclusive learning path. Finally, you'll delve into topic modeling and latent semantic analysis (LSA), implementing algorithms like LDA and NMF in Python. The course is ideal for aspiring data scientists, software engineers, and analysts with basic Python knowledge who want to specialize in NLP. The level is intermediate, and some prior experience in machine learning will help but it is not mandatory.
In this module, we will introduce you to the course and what lies ahead. You’ll gain a clear understanding of the course roadmap and the unique value it offers. We’ll also share a special offer exclusively for enrolled students.
What's included
2 videos2 readings
2 videos•Total 9 minutes
- Introduction and Outline•8 minutes
- Special Offer•1 minute
2 readings•Total 20 minutes
- Introduction to the Course 'NLP – Machine Learning Models in Python'•10 minutes
- Full Course Resources•10 minutes
In this module, we will help you get started by showing you where to access the course code and supporting resources. You'll also receive actionable advice on how to stay engaged and make the most of your learning journey. This foundational setup ensures you're fully prepared for the lessons ahead.
What's included
2 videos1 assignment
2 videos•Total 6 minutes
- Where To Get the Code•3 minutes
- How To Succeed in This Course•3 minutes
1 assignment•Total 15 minutes
- Getting Set Up - Assessment•15 minutes
In this module, we will explore the real-world problem of spam detection using machine learning. You'll gain a solid understanding of the Naive Bayes algorithm, key evaluation metrics, and how to handle class imbalance. The module concludes with a hands-on implementation of a spam classifier in Python.
What's included
6 videos1 assignment
6 videos•Total 60 minutes
- Spam Detection - Problem Description•7 minutes
- Naive Bayes Intuition•12 minutes
- Spam Detection - Exercise Prompt•2 minutes
- Aside: Class Imbalance, ROC, AUC, and F1 Score (pt 1)•12 minutes
- Aside: Class Imbalance, ROC, AUC, and F1 Score (pt 2)•11 minutes
- Spam Detection in Python•16 minutes
1 assignment•Total 15 minutes
- Spam Detection - Assessment•15 minutes
In this module, we will dive into sentiment analysis—a key application of NLP used to determine the emotional tone of text. You’ll learn the intuition and mechanics behind logistic regression and explore both binary and multiclass scenarios. The module wraps up with a guided Python implementation, allowing you to apply these concepts in practice.
What's included
7 videos1 assignment
7 videos•Total 63 minutes
- Sentiment Analysis - Problem Description•7 minutes
- Logistic Regression Intuition (pt 1)•18 minutes
- Multiclass Logistic Regression (pt 2)•7 minutes
- Logistic Regression Training and Interpretation•8 minutes
- Sentiment Analysis - Exercise Prompt•4 minutes
- Sentiment Analysis in Python (pt 1)•11 minutes
- Sentiment Analysis in Python (pt 2)•8 minutes
1 assignment•Total 15 minutes
- Sentiment Analysis - Assessment•15 minutes
In this module, we will explore the field of text summarization and the different strategies used to condense large volumes of text. You'll learn both vector-based methods and the more advanced TextRank algorithm, with intuitive explanations and hands-on Python implementations. This section includes guided exercises for all skill levels, ensuring a strong grasp of summarization techniques.
What's included
10 videos1 assignment
10 videos•Total 70 minutes
- Text Summarization Section Introduction•6 minutes
- Text Summarization Using Vectors•6 minutes
- Text Summarization Exercise Prompt•2 minutes
- Text Summarization in Python•13 minutes
- TextRank Intuition•8 minutes
- TextRank - How It Really Works (Advanced)•11 minutes
- TextRank Exercise Prompt (Advanced)•1 minute
- TextRank in Python (Advanced)•15 minutes
- Text Summarization in Python - The Easy Way (Beginner)•6 minutes
- Text Summarization Section Summary•3 minutes
1 assignment•Total 15 minutes
- Text Summarization - Assessment•15 minutes
In this module, we will dive into topic modeling techniques that help uncover the underlying themes within large text datasets. You'll explore both LDA and NMF, learning the theory, intuition, and practical implementation of each. By the end, you’ll be equipped to apply topic modeling in Python and analyze results effectively.
What's included
9 videos1 assignment
9 videos•Total 64 minutes
- Topic Modeling Section Introduction•3 minutes
- Latent Dirichlet Allocation (LDA) - Essentials•11 minutes
- LDA - Code Preparation•4 minutes
- LDA - Maybe Useful Picture (Optional)•2 minutes
- Latent Dirichlet Allocation (LDA) - Intuition (Advanced)•15 minutes
- Topic Modeling with Latent Dirichlet Allocation (LDA) in Python•12 minutes
- Non-Negative Matrix Factorization (NMF) Intuition•10 minutes
- Topic Modeling with Non-Negative Matrix Factorization (NMF) in Python•6 minutes
- Topic Modeling Section Summary•2 minutes
1 assignment•Total 15 minutes
- Topic Modeling - Assessment•15 minutes
In this module, we will explore Latent Semantic Analysis and Indexing, techniques used to discover hidden patterns and meanings in text data. You'll gain a conceptual understanding of Singular Value Decomposition and how it's applied to NLP tasks. The module includes Python-based implementation and exercises to deepen your practical skills.
What's included
5 videos1 reading3 assignments
5 videos•Total 39 minutes
- LSA / LSI Section Introduction•4 minutes
- SVD (Singular Value Decomposition) Intuition•12 minutes
- LSA / LSI: Applying SVD to NLP•8 minutes
- Latent Semantic Analysis / Latent Semantic Indexing in Python•9 minutes
- LSA / LSI Exercises•6 minutes
1 reading•Total 10 minutes
- Conclusion to the Course 'NLP – Machine Learning Models in Python'•10 minutes
3 assignments•Total 105 minutes
- Latent Semantic Analysis (Latent Semantic Indexing) - Assessment•15 minutes
- Full Course Assessment•60 minutes
- Full Course Practice Assessment•30 minutes
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