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NLP - Natural Language Processing with Python (Udemy.com)

Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing

Created by: Jose Portilla

Last updated April 2023

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What you will learn

  • Learn to work with Text Files with Python
  • Learn how to work with PDF files in Python
  • Utilize Regular Expressions for pattern searching in text
  • Use Spacy for ultra fast tokenization
  • Learn about Stemming and Lemmatization
  • Understand Vocabulary Matching with Spacy
  • Use Part of Speech Tagging to automatically process raw text files
  • Understand Named Entity Recognition
  • Visualize POS and NER with Spacy
  • Use SciKit-Learn for Text Classification
  • Use Latent Dirichlet Allocation for Topic Modelling
  • Learn about Non-negative Matrix Factorization
  • Use the Word2Vec algorithm
  • Use NLTK for Sentiment Analysis
  • Use Deep Learning to build out your own chat bot

How it compares

Against the other 388 rated Udemy courses in Python on CourseDuck:

  • Rating4.6 out of 5higher than 70% of them
  • Reviews20,019#25 of 389 by number of reviews
  • Length11.4 hourslonger than 56% of them
  • Last updatedApril 2023earlier than 52% of them

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Course Description

Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language.

In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python.

We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files.

Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.

We'll understand fundamental NLP concepts such as stemming, lemmatization, stop words, phrase matching, tokenization and more!

Next we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in text to their appropriate part of speech, such as nouns, verbs and adjectives, an essential part of building intelligent language systems.

We'll also learn about named entity recognition, allowing your code to automatically understand concepts like money, time, companies, products, and more simply by supplying the text information.

Through state of the art visualization libraries we will be able view these relationships in real time.

Then we will move on to understanding machine learning with Scikit-Learn to conduct text classification, such as automatically building machine learning systems that can determine positive versus negative movie reviews, or spam versus legitimate email messages.

We will expand this knowledge to more complex unsupervised learning methods for natural language processing, such as topic modelling, where our machine learning models will detect topics and major concepts from raw text files.

This course even covers advanced topics, such as sentiment analysis of text with the NLTK library, and creating semantic word vectors with the Word2Vec algorithm.

Included in this course is an entire section devoted to state of the art advanced topics, such as using deep learning to build out our own chat bots!

Not only do you get fantastic technical content with this course, but you will also get access to both our course related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants.

All of this comes with a 30 day money back garuantee, so you can try the course risk free.

What are you waiting for? Become an expert in natural language processing today!

I will see you inside the course,

Jose


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Instructor Details

Jose Portilla

Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, SalesForce, Starbucks, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings.

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Reviews

4.6

20,019 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Syed Muneeruddin on 2/8/2026

The course a lot of topics going from very easy to complex topics. A variety of topics were covered and the explanation was clear until near the last. But the Deep Learning Section was not explained in detail making it harder to understand. Also the course could be updated to use newer versions of libraries and transformers could be added.

By Jan Purchase on 11/17/2020

This is an excellent course that covers an impressive set of NLP use cases and techniques from the basics to moderately advanced applications. I would highly recommend it to anyone wishing to produce their own document classifiers, QA system, sentiment analyzers or part of speech taggers. My favourite part was a chance to implement a QA engine from a neural architecture outlined in a paper. The course would have got five stars but I could not understand how the instructors implementation fulfilled the design in the paper there were some unexplained inconsistencies. Never the less a great exercise.

By Margaux Mahieu on 3/30/2020

This course was absolutely perfect! I was looking for a course on NLP with which I could learn the bases of NLP such as tokenizer, lemming and all the pipeline for text pre-treatment, but also topic modelling and deep learning and I'm so glad I found this one! The explanations were so clear, the notebooks are built in a very instinctive way so we can find our way around very easily. THANK YOU ! I'll share your course the most I can and surely take another one from you very soon :)

By Szymon on 2/15/2020

I have a mixed feeling about this course. What I was expecting but did not find enough in this course were exercises and/or assignments. Those that were included in the course were not really challenging as they required just repeating the code shown in the lecture. I think this course would greatly benefit from adding some challenging assignments.

By Johannes Verhoeven on 1/18/2020

It gave me a good introduction to machine learning, although very specific (NLP). I have rated this course compared to a number of other Udemy courses I did and then this one jumps out in a positive way. A well-prepared teacher, although he reads displayed sheets out loud: not really best practice. After this course, I recommend to do Kaggle (google it). For the rest thanks very much for a great machine learning introduction, Data Sciences gives me great satisfaction and joy!

By Alex Freberg on 9/24/2019

It covered a lot of the things I was looking to learn. Some of the topics are not super in depth, but more of a walk through with skimming the topics. You will need to go back and do some digging on the topics where this is done. Besides that I learned a lot and can say this class was worth it. Thanks for the good content.

By Vishal Madan on 8/24/2019

The course is Excellent. The last topic of Deep Learning can be a bit overwhelming so I would suggest to take a break after topic 7 and then before going to Deep Learning, learn more about RNNs, keras and tensorflow a bit since the course doesn't cover a lot of basics and assumes that you are familiar. The pace of the course, the notebooks ( though I used Python IDLE ) , the way of explanation is all great. There are lot of topics and I would suggest a refresher through all topics again once you reach the end, maybe without practicing the code part.

By Jose Silvestre Rodriguez Ortiz on 3/26/2019

I love Jose Portilla Courses, I also bought several ones after I started Python from Zero to a Hero. Only thing I would add is that the requirements for this course make things a little confusing at the very end, because when I got to the section of Build a Chat Bot, a couple of things I saw, they were not mentioned during the course, but assuming that the user is already familiar with some Optimizers , Loss function and Activation functions. But this course is a great start for anybody whiling to get into the NLP. GREAT JOB!!!

By Radu Goguta on 1/17/2019

The course is great! I love it! It offers more than a general overview for many concepts that somebody can meet in NLP. It would be nice if in the future Jose and his team will create more courses analyzing each of those concepts deeper and provide more business oriented examples. 5 stars from me.

By Akash Sharma on 1/14/2019

My experience with the course has been overall good. Jose makes the lectures concise and direct to the point. The way the course is organised is also commendable. From the basics of NLP using spacy to making basic chat-bots, this course is a must for someone who wants a good start in NLP.

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Overall Score : 92 / 100