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Self-paced course
Self-paced course
Natural Language Processing in Python
Natural Language Processing in Python
Rating 4.7
15 reviews
15 reviews
15 reviews
Course Description
This is a practical, hands-on course designed to give you a comprehensive overview of all the essential concepts for Natural Language Processing (NLP) in Python.
We’ll start by reviewing the history and evolution of NLP over the past 70 years, including today’s most popular architecture, Transformers. We’ll then walk through the initial text-pre-processing 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.
From there, the course is split into two parts: the first half covers traditional machine-learning techniques and the second half covers modern deep-learning and LLM (large language model) approaches.
For the traditional NLP applications, we’ll begin with Sentiment Analysis to determine text polarity 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 with scikit-learn.
Once you have a solid grasp of foundational NLP concepts, we’ll move on to modern techniques and the data-science mindset shift of the last decade.
We’ll start with the basic building blocks of modern NLP—neural networks. You’ll learn how they’re trained, get familiar with layers, nodes, weights, and activation functions, and see popular deep-learning architectures in action.
Next, we’ll dive into Transformers, the architecture behind LLMs like ChatGPT, Gemini, and Claude. We’ll unpack embeddings, attention, and feed-forward layers, explain encoder-only vs. decoder-only vs. encoder-decoder models, and look at which LLMs fall into each category.
Last but not least, we’ll apply everything in Python using Hugging Face’s transformers library and Model Hub to demo six practical NLP applications: Sentiment Analysis, Named Entity Recognition, Zero-Shot Classification, Text Summarization, Text Generation, and Document Similarity.
If you’re an aspiring or seasoned data scientist seeking a hands-on overview of both traditional and modern NLP techniques in Python, this is the course for you.
Course Description
This is a practical, hands-on course designed to give you a comprehensive overview of all the essential concepts for Natural Language Processing (NLP) in Python.
We’ll start by reviewing the history and evolution of NLP over the past 70 years, including today’s most popular architecture, Transformers. We’ll then walk through the initial text-pre-processing 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.
From there, the course is split into two parts: the first half covers traditional machine-learning techniques and the second half covers modern deep-learning and LLM (large language model) approaches.
For the traditional NLP applications, we’ll begin with Sentiment Analysis to determine text polarity 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 with scikit-learn.
Once you have a solid grasp of foundational NLP concepts, we’ll move on to modern techniques and the data-science mindset shift of the last decade.
We’ll start with the basic building blocks of modern NLP—neural networks. You’ll learn how they’re trained, get familiar with layers, nodes, weights, and activation functions, and see popular deep-learning architectures in action.
Next, we’ll dive into Transformers, the architecture behind LLMs like ChatGPT, Gemini, and Claude. We’ll unpack embeddings, attention, and feed-forward layers, explain encoder-only vs. decoder-only vs. encoder-decoder models, and look at which LLMs fall into each category.
Last but not least, we’ll apply everything in Python using Hugging Face’s transformers library and Model Hub to demo six practical NLP applications: Sentiment Analysis, Named Entity Recognition, Zero-Shot Classification, Text Summarization, Text Generation, and Document Similarity.
If you’re an aspiring or seasoned data scientist seeking a hands-on overview of both traditional and modern NLP techniques in Python, this is the course for you.
Course Content
33.0 video hours
13 assignments & solutions
4 quizes
4 skill assessments
25 CPE credits
Skills you'll learn in this course
Clean, tokenize & vectorize text with pandas, spaCy & scikit‑learn
Build sentiment & topic models using Naïve Bayes, SVM & embeddings
Fine‑tune transformer LLMs with Hugging Face for production NLP
Translate raw text into business insights through real‑world projects
Meet your instructors
Alice Zhao
Lead Data Science Instructor
Alice Zhao is a seasoned data scientist and author of the book, SQL Pocket Guide, 4th Edition (O'Reilly). She has taught numerous courses in Python, SQL, and R as a data science instructor at Maven Analytics and Metis, and as a co-founder of Best Fit Analytics.
Student reviews
The Natural Language Processing in Python course, has built on the foundations laid in the other four data science in Python courses. I thoroughly enjoyed this course. It was eye-opening. The course is broken down into two sections Traditional NLP and Modern NLP. The second section builds on the concepts covered in the first. For me it was insightful to learn that a Logistic Regression is a simple neural network! Alice Zhao was a great teacher. I have learned so much and I'm already applying what I've learned to my own projects.
Andrew Hubbard
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Natural Language Processing in Python
Natural Language Processing in Python
CPE Accreditation
CPE Credits:
25
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
Course Description
This is a practical, hands-on course designed to give you a comprehensive overview of all the essential concepts for Natural Language Processing (NLP) in Python.
We’ll start by reviewing the history and evolution of NLP over the past 70 years, including today’s most popular architecture, Transformers. We’ll then walk through the initial text-pre-processing 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.
From there, the course is split into two parts: the first half covers traditional machine-learning techniques and the second half covers modern deep-learning and LLM (large language model) approaches.
For the traditional NLP applications, we’ll begin with Sentiment Analysis to determine text polarity 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 with scikit-learn.
Once you have a solid grasp of foundational NLP concepts, we’ll move on to modern techniques and the data-science mindset shift of the last decade.
We’ll start with the basic building blocks of modern NLP—neural networks. You’ll learn how they’re trained, get familiar with layers, nodes, weights, and activation functions, and see popular deep-learning architectures in action.
Next, we’ll dive into Transformers, the architecture behind LLMs like ChatGPT, Gemini, and Claude. We’ll unpack embeddings, attention, and feed-forward layers, explain encoder-only vs. decoder-only vs. encoder-decoder models, and look at which LLMs fall into each category.
Last but not least, we’ll apply everything in Python using Hugging Face’s transformers library and Model Hub to demo six practical NLP applications: Sentiment Analysis, Named Entity Recognition, Zero-Shot Classification, Text Summarization, Text Generation, and Document Similarity.
If you’re an aspiring or seasoned data scientist seeking a hands-on overview of both traditional and modern NLP techniques in Python, this is the course for you.
Course Description
This is a practical, hands-on course designed to give you a comprehensive overview of all the essential concepts for Natural Language Processing (NLP) in Python.
We’ll start by reviewing the history and evolution of NLP over the past 70 years, including today’s most popular architecture, Transformers. We’ll then walk through the initial text-pre-processing 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.
From there, the course is split into two parts: the first half covers traditional machine-learning techniques and the second half covers modern deep-learning and LLM (large language model) approaches.
For the traditional NLP applications, we’ll begin with Sentiment Analysis to determine text polarity 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 with scikit-learn.
Once you have a solid grasp of foundational NLP concepts, we’ll move on to modern techniques and the data-science mindset shift of the last decade.
We’ll start with the basic building blocks of modern NLP—neural networks. You’ll learn how they’re trained, get familiar with layers, nodes, weights, and activation functions, and see popular deep-learning architectures in action.
Next, we’ll dive into Transformers, the architecture behind LLMs like ChatGPT, Gemini, and Claude. We’ll unpack embeddings, attention, and feed-forward layers, explain encoder-only vs. decoder-only vs. encoder-decoder models, and look at which LLMs fall into each category.
Last but not least, we’ll apply everything in Python using Hugging Face’s transformers library and Model Hub to demo six practical NLP applications: Sentiment Analysis, Named Entity Recognition, Zero-Shot Classification, Text Summarization, Text Generation, and Document Similarity.
If you’re an aspiring or seasoned data scientist seeking a hands-on overview of both traditional and modern NLP techniques in Python, this is the course for you.
Curriculum
1
Orientation & Benchmark Assessment
1
Orientation & Benchmark Assessment
1
Orientation & Benchmark Assessment
2
Getting Started
2
Getting Started
2
Getting Started
3
Installation & Setup
3
Installation & Setup
3
Installation & Setup
4
Natural Language Processing 101
6 MIN
6 MIN
6 MIN
6 MIN
4
Natural Language Processing 101
6 MIN
6 MIN
6 MIN
6 MIN
4
Natural Language Processing 101
6 MIN
6 MIN
6 MIN
6 MIN
5
Text Preprocessing
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
5
Text Preprocessing
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
5
Text Preprocessing
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6
NLP with Machine Learning
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6
NLP with Machine Learning
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6
NLP with Machine Learning
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
7
Neural Networks & Deep Learning
6 MIN
6 MIN
6 MIN
7
Neural Networks & Deep Learning
6 MIN
6 MIN
6 MIN
7
Neural Networks & Deep Learning
6 MIN
6 MIN
6 MIN
8
Transformers & LLMs
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
8
Transformers & LLMs
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
8
Transformers & LLMs
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
9
Transformers with Hugging Face
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
9
Transformers with Hugging Face
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
9
Transformers with Hugging Face
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
10
NLP Review & Next Steps
6 MIN
10
NLP Review & Next Steps
6 MIN
10
NLP Review & Next Steps
6 MIN
11
Final Assessment
6 MIN
11
Final Assessment
6 MIN
11
Final Assessment
6 MIN
12
Course Feedback & Next Steps
12
Course Feedback & Next Steps
12
Course Feedback & Next Steps
Meet your instructors
Alice Zhao
Lead Data Science Instructor
Alice Zhao is a seasoned data scientist and author of the book, SQL Pocket Guide, 4th Edition (O'Reilly). She has taught numerous courses in Python, SQL, and R as a data science instructor at Maven Analytics and Metis, and as a co-founder of Best Fit Analytics.
Student reviews
The Natural Language Processing in Python course, has built on the foundations laid in the other four data science in Python courses. I thoroughly enjoyed this course. It was eye-opening. The course is broken down into two sections Traditional NLP and Modern NLP. The second section builds on the concepts covered in the first. For me it was insightful to learn that a Logistic Regression is a simple neural network! Alice Zhao was a great teacher. I have learned so much and I'm already applying what I've learned to my own projects.
Andrew Hubbard
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Natural Language Processing in Python
Natural Language Processing in Python
CPE Accreditation
CPE Credits:
25
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
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