NLTK: Build Document Classifier & Spell Checker with Python
NLP with Python - Analyzing Text with the Natural Language Toolkit (NLTK) - Natural Language Processing (NLP) Tutorial
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What You'll Learn in This Course
Master key concepts and practical skills through structured learning modules. By completing this curriculum, you'll gain valuable expertise applicable to real-world scenarios.
Learning Outcome 1
NLTK Main Functions: Concordance, Similar, Lexical Dispersion Plot
Learning Outcome 2
Text Tokenization
Learning Outcome 3
Text Normalization: Stemming & Lemmatization
Learning Outcome 4
Text Tagging: Unigram, N-Gram, Regex
Learning Outcome 5
Text Classification
Skills You'll Develop
This comprehensive NLTK: Build Document Classifier & Spell Checker with Python curriculum is designed to take you from foundational concepts to advanced implementation. Each module builds upon the previous, ensuring a structured learning path that maximizes knowledge retention and practical application.
Course Requirements & Target Audience
Understand what you need to succeed in this course and determine if it's the right fit for your learning goals
Prerequisites & Requirements
What you need before starting this NLTK: Build Document Classifier & Spell Checker with Python course:
- Good Python level. This Natural Language Processing (NLP) tutorial assumes that you already familiar with the basics of writing simple Python programs and that you are generally familiar with Python's core features (data structures, file handling, functions, classes, modules, common library modules, etc.).
- Python 3.4+ (or 2.7). Please note that the tutorial codes are written in Python 3, but it is up to you to fine-tune them if you want to run them on Python 2.
Who This Course Is For
This course is perfect for:
- Motivated learners at any level
- Self-paced students
- Anyone interested in the subject
This course might not be suitable if:
- • You're looking for a quick tutorial rather than comprehensive learning
- • You prefer learning without structured curriculum
- • You need highly specialized or niche content
Course Information & Details
Everything you need to know about this online course, from duration to certification
Course Instructor
GoTrained Academy
Expert instructor with industry experience
Course Language
English
All materials in English
Start Your Learning Journey Today
This online course offers comprehensive training with expert instruction, practical exercises, and a certificate of completion. Join thousands of students advancing their careers through quality online education.
Course Enrollment
Budget-Friendly Course
Typically around $49.99
Pricing may vary. Check the course provider for current promotions and exact pricing.
What's Included
- 5 hours on-demand video
- 4 downloadable resources
- 8 articles
- Access on mobile and TV
- Full lifetime access
- Assignments
Affiliate disclosure: if you enroll through links on this page, the course provider may pay us a commission. This comes at no extra cost to you and does not influence how we describe courses.
- ✓Instant access after enrollment
- ✓Learn at your own pace
- ✓Direct enrollment with Udemy
Where this course fits
Certifications in this area
- Microsoft Certified: Azure Data Fundamentals · Foundational
- Microsoft Certified: Azure AI Fundamentals · Foundational
- AWS Certified AI Practitioner · Foundational
- Microsoft Certified: Fabric Data Engineer Associate · Associate
- Microsoft Certified: Power BI Data Analyst Associate · Associate
- Databricks Certified Data Engineer Associate · Associate
Concepts behind this area
MLOps
MLOps is the engineering practice of keeping a trained model useful in production: recording what produced it, serving it, watching for the day its answers stop matching the world, and retraining or reverting before anyone downstream is harmed by the gap.
Responsible AI
Responsible AI is the practice of building and running systems whose decisions you could defend to the person on the receiving end of them, covering fairness, transparency, privacy and accountability.
Machine Learning
Machine learning is the branch of computing in which a program works out its rule from examples instead of being handed that rule by a programmer, and is judged on how well the rule holds on cases it was never shown.
SQL
SQL is the declarative language for asking questions of relational databases: you state which rows and columns you want and how tables relate, and the database engine works out how to retrieve them.
Data Analysis
Data analysis is the practice of interrogating data to answer a specific question, and of establishing how much weight the answer can bear.
Data Wrangling
Data wrangling is the work of repairing and reshaping real records, and of deciding what their gaps and inconsistencies mean, until a table can be trusted with the question being asked of it.
The field, explained: Artificial Intelligence & Machine Learning · Data & Analytics
Wider context: the fields we cover, the glossary and certification exams, all written by us rather than pulled from a provider feed.
