Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and also to manage Natural Language Processing

BESTSELLER Designed by Jose PortillaLast updated 1/2019 EnglishIncludes

  • 11.5 hours on-demand video
  • 1 article
  • 2 downloadable sources
  • Full lifetime admittance
  • Admittance on mobile and TV
  • Certificate of Achievement

What you’ll get

  • Study to work with Text Files with Python
  • Determine how to work with PDF files in Python
  • Use Regular Expressions for model searching in a text
  • Utilize Spacy for ultra-fast tokenization
  • Study about Stemming and Lemmatization
  • Learn Vocabulary Matching with Spacy
  • Work Part of Speech Tagging to automatically prepare raw text files
  • Get Named Entity Recognition
  • Envision POS and NER with Spacy
  • Utilize SciKit-Learn for Text Classification
  • Apply Latent Dirichlet Allocation for Topic Modelling
  • Study about Non-negative Matrix Factorization
  • Utilize the Word2Vec algorithm
  • Utilize NLTK for Sentiment Analysis
  • Work Deep Learning to build out your own chat bot


  • Learn general Python
  • Have authorities to install python packages onto the computer
  • Internet connection


Welcome to the most reliable Natural Language Processing program on the internet! This program is intended to be your complete online support for determining how to use Natural Language Processing among the Python programming language.


In the program, we will comprise everything you need to study to become a world-class practitioner of NLP among Python.

We’ll begin off with the basics, determining how to open and work with text and PDF files with Python, as well as determining how to use natural expressions to examine for custom patterns inside of text files.

Thereafter, we will begin beside the basics of Natural Language Processing, using the Natural Language Toolkit library for Python, as well as the position of the art Spacy library for ultra-fast tokenization, parsing, object identification, and lemmatization of text.

We’ll learn fundamental NLP theories such as stemming, lemmatization, stop words, phrase matching, tokenization and more

Next, we will incorporate Part-of-Speech tagging, where your Python lines will be able to automatically select words in the text to their relevant part of speech, such as nouns, verbs and adjectives, an imperative part of building intelligent language systems.

We’ll also determine about named entity identification, enabling your code to automatically learn ideas like money, time, companies, products, and also simply by fulfilling the text information.


Within the position of the art visualization libraries, we will be capable view these connections in real-time.

After we will go on to understanding machine learning beside Scikit-Learn to convey text classification, such as automatically building machine learning systems that can learn positive versus negative movie inspections, or spam versus reliable email messages

We will extend this knowledge to more complicated unsupervised education programs for natural language processing, such as topic modeling, where our machine learning models will recognize topics and important ideas from raw text files.

This program even covers advanced topics, such as viewpoint analysis of the text with the NLTK library, and performing semiotic word vectors with the Word2Vec algorithm.

Covered in this program is a whole section applied to the position of the art superior topics, such as utilizing deep learning to make out our personal chatbots

JoseWho this course is for:

  • Python developers are occupied in learning how to utilize Natural Language Processing.

Size: 4.51G



Add a Comment

Your email address will not be published. Required fields are marked *