NLP Training in Sheffield

NLP Training in Sheffield

Natural Language Processing, Natural Language Processing courses

Sheffield

2nd Floor, The Portergate Ecclesall Road
Sheffield, SYK S11 8NX
United Kingdom
South Yorkshire GB
Sheffield
The Ecclesall Road Centre is located within The Portergate building, Sheffield's premier business address. Despite its town centre location the terraced...Read more

Client Testimonials

Neural Networks Fundamentals using TensorFlow as Example

Knowledgeable trainer

Sridhar Voorakkara - INTEL R&D IRELAND LIMITED

Neural Networks Fundamentals using TensorFlow as Example

Topic selection. Style of training. Practice orientation

Commerzbank AG

Neural Networks Fundamentals using TensorFlow as Example

Very good all round overview.Good background into why Tensorflow operates as it does.

Kieran Conboy - INTEL R&D IRELAND LIMITED

Neural Networks Fundamentals using TensorFlow as Example

I liked the opportunities to ask questions and get more in depth explanations of the theory.

Sharon Ruane - INTEL R&D IRELAND LIMITED

Natural Language Processing with Python

I did like the exercises

- Office for National Statistics

Neural Networks Fundamentals using TensorFlow as Example

I was amazed at the standard of this class - I would say that it was university standard.

David Relihan - INTEL R&D IRELAND LIMITED

Neural Networks Fundamentals using TensorFlow as Example

Topic selection. Style of training. Practice orientation

Commerzbank AG

Neural Networks Fundamentals using TensorFlow as Example

Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for

Commerzbank AG

NLP Course Events - Sheffield

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
nlg Python for Natural Language Generation Sheffield 21 hours Wed, 2018-02-14 09:30 £3300 / £3900
nlp Natural Language Processing Sheffield 21 hours Mon, 2018-02-19 09:30 £3300 / £3900
nlpwithr NLP: Natural Language Processing with R Sheffield 21 hours Tue, 2018-02-20 09:30 £3300 / £3900
pythontextml Python: Machine Learning with Text Sheffield 21 hours Tue, 2018-02-20 09:30 £3300 / £3700
python_nltk Natural Language Processing with Python Sheffield 28 hours Mon, 2018-02-26 09:30 £4400 / £5200
tsflw2v Natural Language Processing with TensorFlow Sheffield 35 hours Mon, 2018-02-26 09:30 £6500 / £7500
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Sheffield 28 hours Mon, 2018-02-26 09:30 £5200 / £6000
aitech Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP Sheffield 21 hours Wed, 2018-02-28 09:30 £3900 / £4500
dlfornlp Deep Learning for NLP (Natural Language Processing) Sheffield 28 hours Mon, 2018-03-26 09:30 £4400 / £5200
opennlp OpenNLP for Text Based Machine Learning Sheffield 14 hours Mon, 2018-03-26 09:30 £2200 / £2600
textsum Text Summarization with Python Sheffield 14 hours Mon, 2018-03-26 09:30 £2200 / £2600
nlp Natural Language Processing Sheffield 21 hours Wed, 2018-04-11 09:30 £3300 / £3900
python_nltk Natural Language Processing with Python Sheffield 28 hours Mon, 2018-04-23 09:30 £4400 / £5200
tsflw2v Natural Language Processing with TensorFlow Sheffield 35 hours Mon, 2018-04-23 09:30 £6500 / £7500
aitech Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP Sheffield 21 hours Tue, 2018-04-24 09:30 £3900 / £4500
nlpwithr NLP: Natural Language Processing with R Sheffield 21 hours Tue, 2018-04-24 09:30 £3300 / £3900
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Sheffield 28 hours Mon, 2018-04-30 09:30 £5200 / £6000
nlg Python for Natural Language Generation Sheffield 21 hours Tue, 2018-05-01 09:30 £3300 / £3900
pythontextml Python: Machine Learning with Text Sheffield 21 hours Thu, 2018-05-10 09:30 £3300 / £3700
dlfornlp Deep Learning for NLP (Natural Language Processing) Sheffield 28 hours Mon, 2018-05-21 09:30 £4400 / £5200
nlp Natural Language Processing Sheffield 21 hours Mon, 2018-06-04 09:30 £3300 / £3900
textsum Text Summarization with Python Sheffield 14 hours Tue, 2018-06-05 09:30 £2200 / £2600
aitech Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP Sheffield 21 hours Mon, 2018-06-18 09:30 £3900 / £4500
nlpwithr NLP: Natural Language Processing with R Sheffield 21 hours Mon, 2018-06-18 09:30 £3300 / £3900
python_nltk Natural Language Processing with Python Sheffield 28 hours Mon, 2018-06-25 09:30 £4400 / £5200
tsflw2v Natural Language Processing with TensorFlow Sheffield 35 hours Mon, 2018-06-25 09:30 £6500 / £7500
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Sheffield 28 hours Mon, 2018-06-25 09:30 £5200 / £6000
nlg Python for Natural Language Generation Sheffield 21 hours Tue, 2018-06-26 09:30 £3300 / £3900
opennlp OpenNLP for Text Based Machine Learning Sheffield 14 hours Thu, 2018-06-28 09:30 £2200 / £2600
pythontextml Python: Machine Learning with Text Sheffield 21 hours Mon, 2018-07-02 09:30 £3300 / £3900
dlfornlp Deep Learning for NLP (Natural Language Processing) Sheffield 28 hours Tue, 2018-07-17 09:30 £4400 / £5200
textsum Text Summarization with Python Sheffield 14 hours Tue, 2018-07-31 09:30 £2200 / £2600

Course Outlines

Code Name Duration Outline
nlp Natural Language Processing 21 hours

This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well.

The course will cover how to make use of text written by humans, such as  blog posts, tweets, etc...

For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.

Short Introduction to NLP methods

  • word and sentence tokenization
  • text classification
  • sentiment analysis
  • spelling correction
  • information extraction
  • parsing
  • meaning extraction
  • question answering

Overview of NLP theory

  • probability
  • statistics
  • machine learning
  • n-gram language modeling
  • naive bayes
  • maxent classifiers
  • sequence models (Hidden Markov Models)
  • probabilistic dependency
  • constituent parsing
  • vector-space models of meaning
python_nltk Natural Language Processing with Python 28 hours This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.

Overview of Python packages related to NLP

 

Introduction to NLP (examples in Python of course)

  1. Simple Text Manipulation
    1. Searching Text
    2. Counting Words
    3. Splitting Texts into Words
    4. Lexical dispersion
  2. Processing complex structures
    1. Representing text in Lists
    2. Indexing Lists
    3. Collocations
    4. Bigrams
    5. Frequency Distributions
    6. Conditionals with Words
    7. Comparing Words (startswith, endswith, islower, isalpha, etc...)
  3. Natural Language Understanding
    1. Word Sense Disambiguation
    2. Pronoun Resolution
  4. Machine translations (statistical, rule based, literal, etc...)
  5. Exercises

NLP in Python in examples

  1. Accessing Text Corpora and Lexical Resources
    1. Common sources for corpora
    2. Conditional Frequency Distributions
    3. Counting Words by Genre
    4. Creating own corpus
    5. Pronouncing Dictionary
    6. Shoebox and Toolbox Lexicons
    7. Senses and Synonyms
    8. Hierarchies
    9. Lexical Relations: Meronyms, Holonyms
    10. Semantic Similarity
  2. Processing Raw Text
    1. Priting
    2. struncating
    3. extracting parts of string
    4. accessing individual charaters
    5. searching, replacing, spliting, joining, indexing, etc...
    6. using regular expressions
    7. detecting word patterns
    8. stemming
    9. tokenization
    10. normalization of text
    11. Word Segmentation (especially in Chinese)
  3. Categorizing and Tagging Words
    1. Tagged Corpora
    2. Tagged Tokens
    3. Part-of-Speech Tagset
    4. Python Dictionaries
    5. Words to Propertieis mapping
    6. Automatic Tagging
    7. Determining the Category of a Word (Morphological, Syntactic, Semantic)
  4. Text Classification (Machine Learning)
    1. Supervised Classification
    2. Sentence Segmentation
    3. Cross Validation
    4. Decision Trees
  5. Extracting Information from Text
    1. Chunking
    2. Chinking
    3. Tags vs Trees
  6. Analyzing Sentence Structure
    1. Context Free Grammar
    2. Parsers
  7. Building Feature Based Grammars
    1. Grammatical Features
    2. Processing Feature Structures
  8. Analyzing the Meaning of Sentences
    1. Semantics and Logic
    2. Propositional Logic
    3. First-Order Logic
    4. Discourse Semantics
  9.  Managing Linguistic Data 
    1. Data Formats (Lexicon vs Text)
    2. Metadata
tsflw2v Natural Language Processing with TensorFlow 35 hours

TensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.

Audience

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

  • understand TensorFlow’s structure and deployment mechanisms
  • be able to carry out installation / production environment / architecture tasks and configuration
  • be able to assess code quality, perform debugging, monitoring
  • be able to implement advanced production like training models, embedding terms, building graphs and logging

Getting Started

  • Setup and Installation

TensorFlow Basics

  • Creation, Initializing, Saving, and Restoring TensorFlow variables
  • Feeding, Reading and Preloading TensorFlow Data
  • How to use TensorFlow infrastructure to train models at scale
  • Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics 101

  • Prepare the Data
    • Download
    • Inputs and Placeholders
  • Build the Graph
    • Inference
    • Loss
    • Training
  • Train the Model
    • The Graph
    • The Session
    • Train Loop
  • Evaluate the Model
    • Build the Eval Graph
    • Eval Output

Advanced Usage

  • Threading and Queues
  • Distributed TensorFlow
  • Writing Documentation and Sharing your Model
  • Customizing Data Readers
  • Using GPUs
  • Manipulating TensorFlow Model Files

TensorFlow Serving

  • Introduction
  • Basic Serving Tutorial
  • Advanced Serving Tutorial
  • Serving Inception Model Tutorial

Getting Started with SyntaxNet

  • Parsing from Standard Input
  • Annotating a Corpus
  • Configuring the Python Scripts

Building an NLP Pipeline with SyntaxNet

  • Obtaining Data
  • Part-of-Speech Tagging
  • Training the SyntaxNet POS Tagger
  • Preprocessing with the Tagger
  • Dependency Parsing: Transition-Based Parsing
  • Training a Parser Step 1: Local Pretraining
  • Training a Parser Step 2: Global Training

Vector Representations of Words

  • Motivation: Why Learn word embeddings?
  • Scaling up with Noise-Contrastive Training
  • The Skip-gram Model
  • Building the Graph
  • Training the Model
  • Visualizing the Learned Embeddings
  • Evaluating Embeddings: Analogical Reasoning
  • Optimizing the Implementation

 

 

Neuralnettf Neural Networks Fundamentals using TensorFlow as Example 28 hours

This course will give you knowledge in neural networks and generally in machine learning algorithm,  deep learning (algorithms and applications).

This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.

TensorFlow Basics

  • Creation, Initializing, Saving, and Restoring TensorFlow variables
  • Feeding, Reading and Preloading TensorFlow Data
  • How to use TensorFlow infrastructure to train models at scale
  • Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics

  • Inputs and Placeholders
  • Build the GraphS
    • Inference
    • Loss
    • Training
  • Train the Model
    • The Graph
    • The Session
    • Train Loop
  • Evaluate the Model
    • Build the Eval Graph
    • Eval Output

The Perceptron

  • Activation functions
  • The perceptron learning algorithm
  • Binary classification with the perceptron
  • Document classification with the perceptron
  • Limitations of the perceptron

From the Perceptron to Support Vector Machines

  • Kernels and the kernel trick
  • Maximum margin classification and support vectors

Artificial Neural Networks

  • Nonlinear decision boundaries
  • Feedforward and feedback artificial neural networks
  • Multilayer perceptrons
  • Minimizing the cost function
  • Forward propagation
  • Back propagation
  • Improving the way neural networks learn

Convolutional Neural Networks

  • Goals
  • Model Architecture
  • Principles
  • Code Organization
  • Launching and Training the Model
  • Evaluating a Model
aitech Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP 21 hours

This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.

  1. Distribution big data
    1. Data mining methods (training single systems + distributed prediction: traditional machine learning algorithms + Mapreduce distributed prediction)
    2. Apache Spark MLlib
  2. Recommendations and Advertising:
    1. Natural language
    2. Text clustering, text categorization (labeling), synonyms
    3. User profile restore, labeling system
    4. Recommended algorithms
    5. Insuring the accuracy of "lift" between and within categories
    6. How to create closed loops for recommendation algorithms
  3. Logical regression, RankingSVM,
  4. Feature recognition (deep learning and automatic feature recognition for graphics)
  5. Natural language
    1. Chinese word segmentation
    2. Theme model (text clustering)
    3. Text classification
    4. Extract keywords
    5. Semantic analysis, semantic parser, word2vec (vector to word)
    6. RNN long-term memory (TSTM) architecture
nlpwithr NLP: Natural Language Processing with R 21 hours

It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.

This course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.

By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.

Audience
    Linguists and programmers

Format of the course
    Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

Introduction
    NLP and R vs Python

Installing and configuring R Studio

Installing R packages related to Natural Language Processing (NLP).

An overview of R’s text manipulation capabilities

Getting started with an NLP project in R

Reading and importing data files into R

Text manipulation with R

Document clustering in R

Parts of speech tagging in R

Sentence parsing in R

Working with regular expressions in R

Named-entity recognition in R

Topic modeling in R

Text classification in R

Working with very large data sets

Visualizing your results

Optimization

Integrating R with other languages (Java, Python, etc.)

Closing remarks

pythontextml Python: Machine Learning with Text 21 hours

In this instructor-led, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data.

By the end of this training, participants will be able to:

  • Solve text-based data science problems with high-quality, reusable code
  • Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
  • Build effective machine learning models using text-based data
  • Create a dataset and extract features from unstructured text
  • Visualize data with Matplotlib
  • Build and evaluate models to gain insight
  • Troubleshoot text encoding errors

Audience

  • Developers
  • Data Scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice

Introduction

  • The value of text-based data

Workflow for a Text-Based Data Science Problem

Choosing the Right Machine Learning Libraries

Overview of NLP Techniques

Preparing a Dataset

Visualizing the Data

Working with Text Data with scikit-learn

Building a Machine Learning Model

Splitting into Train and Test Sets

Applying Linear Regression and Non-Linear Regression

Applying NLP Techniques

Parsing Text Data Using Regular Expressions

Exploring Other Machine Language Approaches

Troubleshooting Text Encoding Issues

Closing Remarks

nlg Python for Natural Language Generation 21 hours

Natural language generation (NLG) refers to the production of natural language text or speech by a computer.

In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.

By the end of this training, participants will be able to:

  • Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
  • Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
  • Understand the NLG pipeline and apply the right techniques at each stage
  • Understand the architecture of a Natural Language Generation (NLG) system
  • Implement the most suitable algorithms and models for analysis and ordering
  • Pull data from publicly available data sources as well as curated databases to use as material for generated text
  • Replace manual and laborious writing processes with computer-generated, automated content creation

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice

To request a customized course outline for this training, please contact us.

opennlp OpenNLP for Text Based Machine Learning 14 hours

The Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.

In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.

By the end of this training, participants will be able to:

  • Install and configure OpenNLP
  • Download existing models as well as create their own
  • Train the models on various sets of sample data
  • Integrate OpenNLP with existing Java applications

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice

Introduction to Machine Learning and Natural Language Processing

Installing and Configuring OpenNLP

Overview of OpenNLP's Library Structure

Downloading Existing Models

Calling the OpenNLP's APIs

Sentence Detection and Tokenization

Part-of-Speach (POS) Tagging

Phrase Chunking

Parsing

Name Finding

English Coreference

Training the Tools

Creating a Model from Scratch

Extending OpenNLP

Closing remarks

textsum Text Summarization with Python 14 hours

In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.

In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.

By the end of this training, participants will be able to:

  • Use a command-line tool that summarizes text.
  • Design and create Text Summarization code using Python libraries.
  • Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17

Audience

  • Developers
  • Data Scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice

Introduction to Text Summarization with Python

  • Comparing sample text with auto-generated summaries
  • Installing sumy (a Python Command-Line Executable for Text Summarization)
  • Using sumy as a Command-Line Text Summarization Utility (Hands-On Exercise)

Evaluating three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17 based on documented features

Choosing a library: sumy, pysummarization or readless

Creating a Python application using sumy library on Python 2.7/3.3+

  • Installing the sumy library for Text Summarization
  • Using the Edmundson (Extraction) method in sumy Python Library for Text

Summarization

  • Creating simple Python test code that uses sumy library to generate a text summary

Creating a Python application using pysummarization library on Python 2.7/3.3+

  • Installing pysummarization library for Text Summarization
  • Using the pysummarization library for Text Summarization
  • Creating simple Python test code that uses pysummarization library to generate a text summary

Creating a Python application using readless library on Python 2.7/3.3+

  • Installing readless library for Text Summarization
  • Using the readless library for Text Summarization

Creating simple Python test code that uses readless library to generate a text summary

Troubleshooting and debugging

Closing Remarks

dlfornlp Deep Learning for NLP (Natural Language Processing) 28 hours

Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP.

In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions. 

By the end of this training, participants will be able to:

  • Design and code DL for NLP using Python libraries
  • Create Python code that reads a substantially huge collection of pictures and generates keywords
  • Create Python Code that generates captions from the detected keywords

Audience

  • Programmers with interest in linguistics
  • Programmers who seek an understanding of NLP (Natural Language Processing) 

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice

Introduction to Deep Learning for NLP

Differentiating between the various types of  DL models

Using pre-trained vs trained models

Using word embeddings and sentiment analysis to extract meaning from text 

How Unsupervised Deep Learning works

Installing and Setting Up Python Deep Learning libraries

Using the Keras DL library on top of TensorFlow to allow Python to create captions

Working with Theano (numerical computation library) and TensorFlow (general and linguistics library) to use as extended DL libraries for the purpose of creating captions. 

Using Keras on top of TensorFlow or Theano to quickly experiment on Deep Learning

Creating a simple Deep Learning application in TensorFlow to add captions to a collection of pictures

Troubleshooting

A word on other (specialized) DL frameworks

Deploying your DL application

Using GPUs to accelerate DL

Closing remarks

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