Master NLP with Python: Text Processing & ML Techniques
Focused View
1:44:22
1 -Course Overview.mp4
01:47
2 -Recognizing Natural Language Processing Applications.mp4
06:51
3 -Understanding NLP Tasks.mp4
04:04
4 -Tokenizing Text.mp4
02:38
5 -Removing Stopwords.mp4
02:49
6 -Identifying Bigrams.mp4
02:13
7 -Stemming and POS Tagging.mp4
02:52
8 -Disambiguating Word Meanings.mp4
02:39
9 -Contrasting Rule Based and Machine Learning Approaches.mp4
03:47
10 -Understanding Types of Machine Learning Problems in NLP.mp4
04:51
11 -Understanding the Mechanics of Machine Learning.mp4
03:35
12 -Auto-summarizing Text Using a Rule-based Model.mp4
04:25
13 -Understanding the Mechanics.mp4
03:23
14 -Downloading an Article.mp4
06:11
15 -Preprocessing Article Text.mp4
03:59
16 -Extracting a Summary.mp4
08:45
17 -Outlining the Objective.mp4
02:13
18 -Building a Corpus of Tech Articles.mp4
07:24
19 -Understanding the Clustering Workflow.mp4
10:29
20 -Identifying Themes Using K-Means Clustering.mp4
09:53
21 -Understanding the Classification Workflow.mp4
04:47
22 -Assigning a Theme to an Article.mp4
04:47
More details
Course Overview
This course teaches you to extract valuable insights from raw text data using Python's Natural Language Toolkit (NLTK) and machine learning. Learn web scraping, auto-summarization, and text classification to unlock the power of text data from reviews, tweets, and web content.
What You'll Learn
- Pre-process text using NLTK (tokenization, stemming, POS tagging)
- Scrape and prepare web content with BeautifulSoup
- Build auto-summarization and text classification models
Who This Is For
- Python developers entering NLP field
- Data analysts working with text data
- AI/ML enthusiasts exploring language processing
Key Benefits
- Hands-on experience with real-world text processing
- Master both rule-based and ML approaches
- Build portfolio-ready NLP projects
Curriculum Highlights
- NLP Fundamentals & Text Preprocessing
- Auto-summarization Techniques
- Text Classification with Machine Learning
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Category
- language english
- Training sessions 22
- duration 1:44:22
- level preliminary
- Release Date 2025/06/07
