Learn Natural Language Processing (NLP) and its Python Implementation. Build NLP models.
This course includes:
- 3.5 hours of on-demand video
- 4 articles
- 3 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
4.2
(16 ratings)
3,331 students
Created by Onur Baltacı
What you’ll learn
- Text classification
- Sentiment Analysis
- Working with text data in Python
- Python Fundamentals
- Natural Language Processing (NLP) topics and applications
Course content
12 sections • 36 lectures • 3h 24m total lengthCollapse all sections
Before starting the course1 lecture • 1min
- First LecturePreview00:40
Python Fundamentals9 lectures • 49min
- Print & Comments02:20
- Variables part 106:04
- Variables part 203:58
- Data types part 109:05
- Data types part 2Preview05:06
- Operators08:40
- If statements05:05
- Loops04:27
- Functions04:03
Text Methods3 lectures • 19min
- Text methods part 105:18
- Text methods part 208:25
- Text methods part 305:13
Pandas2 lectures • 16min
- Pandas part 107:19
- Pandas part 208:25
spaCy Library and NLP Concepts2 lectures • 18min
- Introduction to spaCy & Tokenization08:13
- Part of Speech Tagging & Named Entity Recognition09:54
Evaluation of Model Performances2 lectures • 6min
- Train-Test Split04:05
- Confusion Matrix02:14
Data Analysis of Course Data Set3 lectures • 13min
- You can download the course data set00:02
- Data Analysis part 105:43
- Data Analysis part 207:33
Sentiment Analysis2 lectures • 18min
- Sentiment Analysis part 109:11
- Sentiment Analysis part 208:49
Text Classification2 lectures • 14min
- Text Classification part 107:55
- Text Classification part 206:28
NLP Project4 lectures • 13min
- You can download the data set00:03
- Exploring data03:38
- Sentiment analysis04:08
- Text ClassificationPreview04:42
NLP Project 25 lectures • 37min
- You can download the data set00:02
- Data Analysis part 110:26
- Data Analysis part 206:54
- Sentiment Analysis09:42
- Text Classification10:10
Bonus Section1 lecture • 1min
- bonus lecture00:11
Requirements
- No knowledge is required. Just willingness to learn is enough.
Description
Welcome to the landing page of the Python for Natural Language Processing (NLP) course. This course is built for students who want to learn NLP concepts in Python. The course starts with a repeat of the Python Fundamentals. After that test methods and the panda’s library is covered in the course. Text methods will be helpful when we are going to be building Natural Language Processing projects. We will use panda’s library for reading and analyzing our data sets. After it, we will cover some features of the spaCy library like part of speech tagging, tokenization, and named entity recognition. spaCy with NLTK is the both most popular Python library for Natural Language Processing. After covering that concept we will move into the evaluation of model performances section and there we will be learning how the NLP models will be evaluated. After that task, we will see Sentiment Analysis and Text Classification and we will make examples of them. At the final lectures of the course, we will build a Natural Language Processing project from scratch with what we learned through the course and we will finish. During the whole course process and after it, students can reach out to me about the course concepts via the Q&A section of the course or direct messages on Udemy. Thanks for visiting the course page and reading the course description.
Who this course is for:
- People who are interested in Data Science and want to learn Natural Language Processing (NLP)
