Easy Natural Language Processing (NLP) in Python

Easy Natural Language Processing (NLP) in Python

2 Hours
Deal Price$120.00
You save 0%
Easy Natural Language Processing (NLP) in Python
$120.00
Easy Natural Language Processing (NLP) in Python

19 Lessons (2h)

  • Natural Language Processing - What is it used for?
    Introduction and Outline3:04
    NLP Applications6:40
    Why is NLP hard?2:30
  • Build your own spam detector
    Build your own spam detector - description of data2:08
    Build your own spam detector - the code6:16
    Other types of features1:30
  • Build your own sentiment analyzer
    Description of Sentiment Analyzer3:13
    Sentiment Analysis in Python19:48
  • NLTK Exploration
    NLTK Exploration: POS Tagging2:00
    NLTK Exploration: Stemming and Lemmatization2:06
    NLTK Exploration: Named Entity Recognition3:13
  • Latent Semantic Analysis
    Latent Semantic Analysis - What does it do?2:30
    PCA and SVD - The underlying math behind LSA7:59
    Latent Semantic Analysis in Python10:08
  • Write your own article spinner
    Article Spinning Introduction2:43
    Trigram Model2:12
    Writing an article spinner in Python11:33
  • How to learn more about NLP
    What we didn't talk about2:45
  • Appendix
    How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow17:22
Easy Natural Language Processing (NLP) in Python
$120.00
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DescriptionInstructorImportant DetailsRelated Products

Explore & Build Common Applications of Machine Learning in Industry

LP
Lazy ProgrammerThe Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Description

Over this course you will build multiple practical systems using natural language processing (NLP), the branch of machine learning and data science that deals with text and speech. You'll start with a background on NLP before diving in, building a spam detector and a model for sentiment analysis in Python. Learning how to build these practical tools will give you an excellent window into the mechanisms that drive machine learning.

  • Access 19 lectures & 2 hours of content 24/7
  • Build a spam detector & sentiment analysis model that may be used to predict the stock market
  • Learn practical tools & techniques like the natural language toolkit library & latent semantic analysis
  • Create an article spinner from scratch that can be used as an SEO tool
Think this is cool? Check out the other bundles in this series, The Deep Learning and Artificial Intelligence Introductory Bundle, and The Advanced Guide to Deep Learning and Artificial Intelligence.

Specs

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels, but knowledge of Python and Numpy coding is expected
  • All code for this course is available for download here, in the directory nlp_class

Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
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