Natural Language Processing In Python (New For 2025!)

Learn NLP in Python, including text cleaning, machine learning, transformers & LLMs using scikit-learn and Hugging Face

What you'll learn

Review the history and evolution of NLP techniques and applications, from traditional machine learning models to modern LLM approaches

Walk through the NLP text preprocessing pipeline, including cleaning, normalization, linguistic analysis, and vectorization

Use traditional machine learning techniques to perform sentiment analysis, text classification, and topic modeling

Understand the theory behind neural networks and deep learning, the building blocks of modern NLP techniques

Break down the main parts of the Transformers architecture, including embeddings, attention and feedforward neural networks (FFNs)

Use pretrained LLMs with Hugging Face to perform sentiment analysis, NER, zero-shot classification, document similarity, and text summarization & generation

Requirements

We strongly recommend taking our Data Prep & EDA with Python course first

Jupyter Notebooks (free download, we'll walk through the install)

Familiarity with base Python and Pandas is recommended, but not required

Description

This is a practical, hands-on course designed to give you a comprehensive overview of all the essential concepts for modern Natural Language Processing (NLP) in Python.We’ll start by reviewing the history and evolution of NLP over the past 70 years, including the most popular architecture at the moment, Transformers. We'll also walk through the initial text preprocessing steps required for modeling, where you’ll learn how to clean and normalize data with pandas and spaCy, then vectorize that data into a Document-Term Matrix using both word counts and TF-IDF scores.After that, the course is split into two parts:The first half covers traditional machine learning techniquesThe second half covers modern deep learning and LLM (large language model) approachesFor the traditional NLP applications, we'll begin with Sentiment Analysis to determine the positivity or negativity of text using the VADER library. Then we’ll cover Text Classification on labeled data with Naïve Bayes, as well as Topic Modeling on unlabeled data using Non-Negative Matrix Factorization, all using the scikit-learn library.Once you have a solid understanding of the foundational NLP concepts, we’ll move on to the second half of the course on modern NLP techniques, which covers the major advancements in NLP and the data science mindset shift over the past decade.We’ll start with the basic building blocks of modern NLP techniques, which are neural networks. You’ll learn how neural networks are trained, become familiar with key terms like layers, nodes, weights, and activation functions, and then get introduced to popular deep learning architectures and their practical applications.After that, we’ll talk about Transformers, the architectures behind popular LLMs like ChatGPT, Gemini, and Claude. We’ll cover how the main layers work and what they do, including embeddings, attention, and feedforward neural networks. We’ll also review the differences between encoder-only, decoder-only, and encoder-decoder models, and the types of LLMs that fall into each category.Last but not least, we’re going to apply what we’ve learned with Python. We’ll be using Hugging Face’s Transformers library and their Model Hub to demo six practical NLP applications, including Sentiment Analysis, Named Entity Recognition, Zero-Shot Classification, Text Summarization, Text Generation, and Document Similarity.COURSE OUTLINE:Installation & SetupInstall Anaconda, start writing Python code in a Jupyter Notebook, and learn how to create a new conda environment to get set up for this courseNatural Language Processing 101Review the basics of natural language processing (NLP), including key concepts, the evolution of NLP over the years, and its applications & Python librariesText PreprocessingWalk through the text preprocessing steps required before applying machine learning algorithms, including cleaning, normalization, vectorization, and moreNLP with Machine LearningPerform sentiment analysis, text classification, and topic modeling using traditional NLP methods, including rules-based, supervised, and unsupervised machine learning techniquesNeural Networks & Deep LearningVisually break down the concepts behind neural networks and deep learning, the building blocks of modern NLP techniquesTransformers & LLMsDive into the main parts of the transformer architecture, including embeddings, attention, and FFNs, as well as popular LLMs for NLP tasks like BERT, GPT, and moreHugging Face TransformersIntroduce the Hugging Face Transformers library in Python and walk through examples of how you can use pretrained LLMs to perform NLP tasks, including sentiment analysis, named entity recognition (NER), zero-shot classification, text summarization, text generation, and document similarityNLP Review & Next StepsReview the NLP techniques covered in this course, when to use them, and how to dive deeper and stay up-to-date__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:12.5 hours of high-quality video13 homework assignments4 interactive exercisesNatural Language Processing in Python ebook (200+ pages)Downloadable project files & solutionsExpert support and Q&A forum30-day Udemy satisfaction guaranteeIf you're an aspiring or seasoned data scientist looking for a practical overview of both traditional and modern NLP techniques in Python, this is the course for you.Happy learning!-Alice Zhao (Python Expert & Data Science Instructor, Maven Analytics)

Overview

Section 1: Getting Started

Lecture 1 Course Introduction

Lecture 2 About This Series

Lecture 3 Course Structure & Outline

Lecture 4 READ ME: Important Notes for New Students

Lecture 5 DOWNLOAD: Course Resources

Lecture 6 The Course Assignments

Section 2: Installation & Setup

Lecture 7 Section Introduction

Lecture 8 Anaconda Overview

Lecture 9 Installing Anaconda

Lecture 10 Launching Jupyter Notebook

Lecture 11 Conda Environments

Lecture 12 Conda Workflow

Lecture 13 Conda Commands

Lecture 14 DEMO: Create a Conda Environment

Lecture 15 Environments in This Course

Section 3: Natural Language Processing 101

Lecture 16 Section Introduction

Lecture 17 Intro to NLP

Lecture 18 History of NLP

Lecture 19 NLP Applications & Techniques

Lecture 20 NLP Libraries in Python

Lecture 21 Key Takeaways

Section 4: Text Preprocessing

Lecture 22 Section Introduction

Lecture 23 NLP Pipeline

Lecture 24 Text Preprocessing Overview

Lecture 25 ASSIGNMENT: Create a New Environment

Lecture 26 SOLUTION: Create a New Environment

Lecture 27 Text Preprocessing with Pandas

Lecture 28 DEMO: Text Preprocessing Setup

Lecture 29 DEMO: Text Preprocessing with Pandas

Lecture 30 PRO TIP: Create a Function

Lecture 31 ASSIGNMENT: Text Preprocessing with Pandas

Lecture 32 SOLUTION: Text Preprocessing with Pandas

Lecture 33 Text Preprocessing with spaCy

Lecture 34 Tokenization

Lecture 35 Lemmatization

Lecture 36 Stop Words

Lecture 37 Parts of Speech Tagging

Lecture 38 DEMO: Tokens, Lemmas & Stop Words

Lecture 39 PRO TIP: Use the Apply Method

Lecture 40 DEMO: Parts of Speech Tagging

Lecture 41 DEMO: Create an NLP Pipeline

Lecture 42 ASSIGNMENT: Text Preprocessing with spaCy

Lecture 43 SOLUTION: Text Preprocessing with spaCy

Lecture 44 Vectorization

Lecture 45 Count Vectorizer in Python

Lecture 46 DEMO: Count Vectorizer

Lecture 47 DEMO: Count Vectorizer Parameters

Lecture 48 PRO TIP: Exploratory Data Analysis

Lecture 49 ASSIGNMENT: Count Vectorizer

Lecture 50 SOLUTION: Count Vectorizer

Lecture 51 TF-IDF

Lecture 52 TF-IDF Vectorizer in Python

Lecture 53 DEMO: TF-IDF Vectorizer

Lecture 54 ASSIGNMENT: TF-IDF Vectorizer

Lecture 55 SOLUTION: TF-IDF Vectorizer

Lecture 56 Key Takeaways

Section 5: NLP with Machine Learning

Lecture 57 Section Introduction

Lecture 58 What is Machine Learning?

Lecture 59 Common ML Algorithms for NLP

Lecture 60 Traditional NLP Overview

Lecture 61 Traditional vs Modern NLP

Lecture 62 DEMO: Create a New Environment

Lecture 63 Sentiment Analysis

Lecture 64 Sentiment Analysis in Python

Lecture 65 DEMO: Sentiment Analysis in Python

Lecture 66 ASSIGNMENT: Sentiment Analysis

Lecture 67 SOLUTION: Sentiment Analysis

Lecture 68 Text Classification Basics

Lecture 69 Text Classification Algorithms

Lecture 70 Naïve Bayes

Lecture 71 Naïve Bayes in Python

Lecture 72 DEMO: Naïve Bayes Setup

Lecture 73 DEMO: Naïve Bayes Workflow

Lecture 74 DEMO: Naïve Bayes Prediction

Lecture 75 PRO TIP: Compare ML Models

Lecture 76 Text Classification Next Steps

Lecture 77 ASSIGNMENT: Text Classification

Lecture 78 SOLUTION: Text Classification

Lecture 79 Topic Modeling Basics

Lecture 80 Topic Modeling Algorithms

Lecture 81 Non-Negative Matrix Factorization (NMF)

Lecture 82 NMF in Python

Lecture 83 DEMO: Fit an NMF Model

Lecture 84 PRO TIP: Display Topics Function

Lecture 85 DEMO: Tune an NMF Model

Lecture 86 Topic Modeling Next Steps

Lecture 87 PRO TIP: Combine ML Algorithms

Lecture 88 ASSIGNMENT: Topic Modeling

Lecture 89 SOLUTION: Topic Modeling

Lecture 90 Key Takeaways

Section 6: Neural Networks & Deep Learning

Lecture 91 Section Introduction

Lecture 92 Modern NLP Overview

Lecture 93 Intro to Neural Networks

Lecture 94 Logistic Regression Refresher

Lecture 95 Logistic Regression: Visually Explained

Lecture 96 Neural Networks: Visually Explained

Lecture 97 Neural Network Summary

Lecture 98 EXERCISE: Neural Network Components

Lecture 99 SOLUTION: Neural Network Components

Lecture 100 Neural Networks in Python

Lecture 101 DEMO: Neural Networks in Python

Lecture 102 DEMO: Neural Network Matrices

Lecture 103 PRO TIP: NN Notation & Matrices

Lecture 104 How a Neural Network is Trained

Lecture 105 Neural Network Training: Visually Explained

Lecture 106 EXERCISE: Neural Network Training

Lecture 107 SOLUTION: Neural Network Training

Lecture 108 Intro to Deep Learning

Lecture 109 Deep Learning Architectures

Lecture 110 Deep Learning in Practice

Lecture 111 Pretrained Deep Learning Models

Lecture 112 EXERCISE: Deep Learning Concepts

Lecture 113 SOLUTION: Deep Learning Concepts

Lecture 114 Key Takeaways

Section 7: Transformers & LLMs

Lecture 115 Section Introduction

Lecture 116 Modern NLP Recap

Lecture 117 Transformers & LLMs Overview

Lecture 118 Transformer Architecture

Lecture 119 Transformer Architecture | Embeddings

Lecture 120 Transformer Architecture | Attention

Lecture 121 Transformer Architecture | Feedforward Neural Network

Lecture 122 Transformers Summary

Lecture 123 Breaking Down the Transformer Diagram

Lecture 124 Encoders & Decoders

Lecture 125 Large Language Models (LLMs)

Lecture 126 EXERCISE: Transformers & LLMs Concepts

Lecture 127 SOLUTION: Transformers & LLMs Concepts

Lecture 128 Key Takeaways

Section 8: Transformers with Hugging Face

Lecture 129 Section Introduction

Lecture 130 Hugging Face Overview

Lecture 131 DEMO: Create a New Environment

Lecture 132 Sentiment Analysis with LLMs

Lecture 133 DEMO: Basic Sentiment Analysis Pipeline

Lecture 134 DEMO: Timing, Logging and Device Setup

Lecture 135 DEMO: Compare Sentiment Scores

Lecture 136 PRO TIP: Speed Up Transformers Code

Lecture 137 ASSIGNMENT: Sentiment Analysis with LLMs

Lecture 138 SOLUTION: Sentiment Analysis with LLMs

Lecture 139 Named Entity Recognition

Lecture 140 DEMO: Basic NER Pipeline

Lecture 141 DEMO: Hugging Face Model Hub

Lecture 142 DEMO: Clean NER Output

Lecture 143 ASSIGNMENT: Named Entity Recognition

Lecture 144 SOLUTION: Named Entity Recognition

Lecture 145 Zero-Shot Classification

Lecture 146 DEMO: Zero-Shot Classification

Lecture 147 ASSIGNMENT: Zero-Shot Classification

Lecture 148 SOLUTION: Zero-Shot Classification

Lecture 149 Text Summarization

Lecture 150 DEMO: Basic Text Summarization Pipeline

Lecture 151 DEMO: Multiple Pipelines

Lecture 152 ASSIGNMENT: Text Summarization

Lecture 153 SOLUTION: Text Summarization

Lecture 154 PRO TIP: Text Generation

Lecture 155 Document Embeddings

Lecture 156 Cosine Similarity

Lecture 157 Document Similarity with Embeddings

Lecture 158 DEMO: Feature Extraction & Embeddings

Lecture 159 DEMO: Cosine & Document Similarity

Lecture 160 PRO TIP: Recommender Function

Lecture 161 ASSIGNMENT: Document Similarity

Lecture 162 SOLUTION: Document Similarity

Lecture 163 Key Takeaways

Section 9: NLP Review & Next Steps

Lecture 164 NLP Review & Flow Chart

Lecture 165 NLP Next Steps

Lecture 166 BONUS LESSON

Aspiring Data Scientists who want a practical overview of natural language processing techniques in Python,Seasoned Data Scientists looking to learn the latest NLP techniques, such as Transformers, LLMs and Hugging Face