Overview

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.

Syllabus

Welcome

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.

Getting Set Up

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.

Spam Detection

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.

Sentiment Analysis

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.

Text Summarization

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.

Topic Modeling

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.

Latent Semantic Analysis (Latent Semantic Indexing)

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.