- Introduction and Outline
- Why learn NPL?
- The Central Message of this Course (Big Picture Perspective)
Online
₹ 455 3,499
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Data Science: Natural Language Processing (NPL) in Python online certification is a course that employs a functional computing learning technique that has proven to be extremely efficient among hundreds of students who took the course. The course is developed by Lazy Programmer Inc. - Artificial intelligence and machine learning engineer and presented by Udemy, an online course provider aimed at professionals and beginners.
Data Science: Natural Language Processing (NPL) in Python online training will help learners to develop a cypher decryption algorithm to be used in warfare and espionage, how to build and use a variety of useful Natural Language Processing tools, such as character-level language models (based on the Markov principle) and evolutionary algorithms. Learners will develop a spam detector using conventional machine learning techniques, and develop a python sentiment analysis model to provide a value to a body of text indicating how good or bad it is.
Candidates can enrol themselves in the Data Science: Natural Language Processing (NPL) in Python online course and are advised to have prior knowledge of python coding so that lack of knowledge of particular topics does not impair their overall experience.
The highlights
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 12 hours of pre-recorded video content
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and TV
Program offerings
- Certificate of completion
- Self-paced course
- English videos
- Multi-language subtitles
- 12 hours of pre-recorded video content
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
After completing the Data Science: Natural Language Processing (NPL) in Python certification course, learners will be able to create their cypher decryption algorithm utilising evolutionary algorithms and Markov models for language modelling, create their spam detection program in python and python sentiment analysis program. Candidates will also be able to carry out latent semantic analysis or latent semantic indexing.
Who it is for
The syllabus
Natural Language Processing - What is it used for?
Course Preparation
- How to Succeed in this Course
- Where to get the code and data
- How to Open Files for Windows Users
Machine Learning Basics Review
- Machine Learning: Section Introduction
- What is Classification?
- Classification in Code
- What is Regression?
- Regression in Code
- What is a Feature Vector?
- Machine Learning is Nothing but Geometry
- All Data is the Same
- Comparing Different Machine Learning Models
- Machine Learning and Deep Learning: Future Topics
- Section Summary
Markov Models
- Markov Models Section Introduction
- The Markov Property
- The Markov Model
- Probability Smoothing and Log-Probabilities
- Building a Text Classifier (Theory)
- Building a Text Classifier (Exercise Prompt)
- Building a Text Classifier (Code pt 1)
- Building a Text Classifier (Code pt 2)
- Language Model (Theory)
- Language Model (Exercise Prompt)
- Language Model (Code pt 1)
- Language Model (Code pt 2)
- Markov Models Section Summary
Decrypting Ciphers
- Section Introduction
- Ciphers
- Language Models
- Genetic Algorithms
- Code Preparation
- Code pt 1
- Code pt 2
- Code pt 3
- Code pt 4
- Code pt 5
- Code pt 6
- Section Conclusion
Build your own spam detector
- Build your own spam detector - description of data
- Build your own spam detector using Naive Bayes and AdaBoost - the code
- Key Takeaway from Spam Detection Exercise
- Naive Bayes Concepts
- AdaBoost Concepts
- Other types of features
- Spam Detection FAQ (Remedial #1)
- What is a Vector?
- SMS Spam Example
- SMS Spam in Code
- Suggestion Box
Build your own sentiment analyzer
- Description of Sentiment Analyzer
- Logistic Regression Review
- Preprocessing: Tokenization
- Preprocessing: Tokens to Vectors
- Sentiment Analysis in Python using Logistic Regression
- Sentiment Analysis Extension
- How to Improve Sentiment Analysis & FAQ
NLTK Exploration
- NLTK Exploration: POS Tagging
- NLTK Exploration: Stemming and Lemmatization
- NLTK Exploration: Named Entity Recognition
- Want more NLTK?
Latent Semantic Analysis
- Latent Semantic Analysis - What does it do?
- SVD - The underlying math behind LSA
- Latent Semantic Analysis in Python
- What is Latent Semantic Analysis Used For?
- Extending LSA
Write your own article spinner
- Article Spinning Introduction and Markov Models
- Trigram Model
- More about Language Models
- Precode Exercises
- Writing an article spinner in Python
- Article Spinner Extension Exercises
How to learn more about NLP1
- What we didn't talk about
Setting Up Your Environment (FAQ by Student Request)
- Anaconda Environment Setup
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Extra Help With Python Coding for Beginners (FAQ by Student Request)
- How to Code by Yourself (part 1)
- How to Code by Yourself (part 2)
- Proof that using Jupyter Notebook is the same as not using it
- Python 2 vs Python 3
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
- How to Succeed in this Course (Long Version)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
- Machine Learning and AI Prerequisite Roadmap (pt 1)
- Machine Learning and AI Prerequisite Roadmap (pt 2)
Appendix / FAQ Finale
- What is the Appendix?
- BONUS