Natural Language Processing In Python (New For 2025!)
Published 5/2025
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.46 GB | Duration: 12h 37m
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 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
