A Hands-on Introduction to Natural Language Processing

schedule Aug 7th 10:00 AM - 06:00 PM place Jupiter 1 people 1 Interested

Data is the new oil and unstructured data, especially text, images and videos contain a wealth of information. However, due to the inherent complexity in processing and analyzing this data, people often refrain from spending extra time and effort in venturing out from structured datasets to analyze these unstructured sources of data, which can be a potential gold mine. Natural Language Processing (NLP) is all about leveraging tools, techniques and algorithms to process and understand natural language-based data, which is usually unstructured like text, speech and so on. In this workshop, we will be looking at tried and tested strategies, techniques and workflows which can be leveraged by practitioners and data scientists to extract useful insights from text data.

Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive case- studies and hands-on examples to master state-of-the-art tools, techniques and frameworks for actually applying NLP to solve real- world problems. We leverage Python 3 and the latest and best state-of- the-art frameworks including NLTK, Gensim, SpaCy, Scikit-Learn, TextBlob, Keras and TensorFlow to showcase our examples.

In my journey in this field so far, I have struggled with various problems, faced many challenges, and learned various lessons over time. This workshop will contain a major chunk of the knowledge I’ve gained in the world of text analytics and natural language processing, where building a fancy word cloud from a bunch of text documents is not enough anymore. Perhaps the biggest problem with regard to learning text analytics is not a lack of information but too much information, often called information overload. There are so many resources, documentation, papers, books, and journals containing so much content that they often overwhelm someone new to the field. You might have had questions like ‘What is the right technique to solve a problem?’, ‘How does text summarization really work?’ and ‘Which are the best frameworks to solve multi-class text categorization?’ among many other questions! Based on my prior knowledge and learnings from publishing a couple of books in this domain, this workshop should help readers avoid the pressing issues I’ve faced in my journey so far and learn the strategies to master NLP.

This workshop follows a comprehensive and structured approach. First it tackles the basics of natural language understanding and Python for handling text data in the initial chapters. Once you’re familiar with the basics, we cover text processing, parsing and understanding. Then, we address interesting problems in text analytics in each of the remaining chapters, including text classification, clustering and similarity analysis, text summarization and topic models, semantic analysis and named entity recognition, sentiment analysis and model interpretation. The last chapter is an interesting chapter on the recent advancements made in NLP thanks to deep learning and transfer learning and we cover an example of text classification with universal sentence embeddings.

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Outline/Structure of the Workshop

The following is the rough structure of the workshop

  1. Introduction to Natural Language Processing
  2. Text pre-processing and Wrangling
    • Removing HTML tags\noise
    • Removing accented characters
    • Removing special characters\symbols
    • Handling contractions
    • Stemming
    • Lemmatization
    • Stop word removal
  3. Project: Build a duplicate character removal module
  4. Project: Build a spell-check and correction module
  5. Project: Build an end-to-end text pre-processor
  6. Text Understanding
    • POS (Parts of Speech) Tagging
    • Text Parsing
      • Shallow Parsing
      • Dependency Parsing
      • Constituency Parsing
    • NER (Named Entity Recognition) Tagging
  7. Project: Build your own POS Tagger
  8. Project: Build your own NER Tagger
  9. Text Representation – Feature Engineering
    • Traditional Statistical Models – BOW, TF-IDF
    • Newer Deep Learning Models for word embeddings – Word2Vec, GloVe, FastText
  10. Project: Similarity and Movie Recommendations
  11. Project: Interactive exploration of Word Embeddings
  12. Case Studies for other common NLP Tasks
    • Project: Sentiment Analysis using unsupervised learning and supervised learning (machine and deep learning)
    • Project: Text Clustering (grouping similar movies)
    • Project: Text Summarization and Topic Models
  13. Promise of Deep Learning for NLP, Transfer and Generative Learning
  14. Hands-on with universal sentence embeddings in deep learning

Learning Outcome

  • Learn and understand popular NLP workflows with interactive examples
  • Covers concepts and interactive projects on cleaning and handling noisy unstructured text data including duplicate checks, spelling corrections and text wrangling
  • Build your own POS and NER taggers and parse text data to understand it better
  • Understand, build and explore text semantics and representations with traditional statistical models and newer word embedding models
  • Projects on popular NLP tasks including text classification, sentiment analysis, text clustering, summarization, topic models and recommendations
  • Recent state-of-the-art cutting edge research implementation on deep transfer learning for NLP

Target Audience

Data Scientists, Engineers, Developers, AI Enthusiasts, Linguistic Experts

Prerequisites for Attendees

Basic knowledge of Python and Machine Learning.

All the examples will be covered in Python

schedule Submitted 4 months ago

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      This workshop will focus on building such a knowledge graph from unstructured text.

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      With the advent of next generation sequencing, high through put analysis, enhanced computing power and new aspirations with neural network to address this conundrum of complicated genetic elements (structure and function of various genes in our systems). This requires the genomic material extraction, their sequencing (automated system) and analysis to map the strings of As, Ts, Gs, and Cs which yields genomic dataset. These datasets are too large for traditional and applied statistical techniques. Consequently, the important signals are often incredibly small along with blaring technical noise. This further requires far more sophisticated analysis techniques. Artificial intelligence and deep learning gives us the power to draw clinically useful information from the genetic datasets obtained by sequencing.

      Precision of these analyses have become vital and way forward for disease detection, its predisposition, empowers medical authorities to make fair and situationally decision about patient treatment strategies. This kind of genomic profiling, prediction and mode of disease management is useful to tailoring FDA approved treatment strategies based on these molecular disease drivers and patient’s molecular makeup.

      Now, the present scenario encourages designing, developing, testing of medicine based on existing genetic insights and models. Deep learning models are helping to analyze and interpreting tiny genetic variations ( like SNPs – Single Nucleotide Polymorphisms) which result in unraveling of crucial cellular process like metabolism, DNA wear and tear. These models are also responsible in identifying disease like cancer risk signatures from various body fluids. They have the immense potential to revolutionize healthcare ecosystem. Clinical data collection is not streamlined and done in a haphazard manner and the requirement of data to be amenable to a uniform fetchable and possibility to be combined with genetic information would power the value, interpretation and decisive patient treatment modalities and their outcomes.

      There is hugh inflow of medical data from emerging human wearable technologies, along with other health data integrated with ability to do quickly carry out complex analyses on rich genomic databases over the cloud technologies … would revitalize disease fighting capability of humans. Last but still upcoming area of application in direct to consumer genomics (success of 23andMe).

      This road map promises an end-to-end system to face disease in its all forms and nature. Medical research, and its applications like gene therapies, gene editing technologies like CRISPR, molecular diagnostics and precision medicine could be revolutionized by tailoring a high-throughput computing method and its application to enhanced genomic datasets.

    • Liked Saurabh Jha

      Saurabh Jha / Usha Rengaraju - Hands on Deep Learning for Computer Vision

      480 Mins

      Computer Vision has lots of applications including medical imaging, autonomous vehicles, industrial inspection and augmented reality. Use of Deep Learning for computer Vision can be categorized into multiple categories for both images and videos – Classification, detection, segmentation & generation.

      Having worked in Deep Learning with a focus on Computer Vision have come across various challenges and learned best practices over a period experimenting with cutting edge ideas. This workshop is for Data Scientists & Computer Vision Engineers whose focus is deep learning. We will cover state of the art architectures for Image Classification, Segmentation and practical tips & tricks to train a deep neural network models. It will be hands on session where every concepts will be introduced through python code and our choice of deep learning framework will be PyTorch v1.0.

      The workshop takes a structured approach. First it covers basic techniques in image processing and python for handling images and building Pytorch data loaders. Then we introduce how to build image classifier followed by how segmentation was done in pre CNN era and cover clustering techniques for segmentation. Start with basics of neural networks and introduce Convolutional neural networks and cover advanced architecture – Resnet. Introduce the idea of Fully Convolutional Paper and it’s impact on Semantic Segmentation. Cover latest semantic segmentation architecture with code and basics of scene text understanding in pytorch with how to run carefully designed experiments using callbacks, hooks. Introduce discriminative learning rate and mixed precision to train deep neural network models. Idea is to bridge the gap between theory and practice and teach how to run practical experiments and tune deep learning based systems by covering tricks introduced in various research papers. Discuss in-depth on the interaction between batchnorm, weight decay and learning rate.

    • Liked Ramanathan R

      Ramanathan R / Gurram Poorna Prudhvi - Time Series analysis in Python

      480 Mins

      “Time is precious so is Time Series Analysis”

      Time series analysis has been around for centuries helping us to solve from astronomical problems to business problems and advanced scientific research around us now. Time stores precious information, which most machine learning algorithms don’t deal with. But time series analysis, which is a mix of machine learning and statistics helps us to get useful insights. Time series can be applied to various fields like economy forecasting, budgetary analysis, sales forecasting, census analysis and much more. In this workshop, We will look at how to dive deep into time series data and make use of deep learning to make accurate predictions.

      Structure of the workshop goes like this

      • Basics of time series analysis
      • Understanding Time series data with pandas
      • Preprocessing Time Series data
      • Classical Time series models (AR, MA, ARMA, ARIMA, SARIMA, GARCH, E-GARCH)
      • Forecasting with MLP (Multi-Layer Perceptron)
      • Forecasting with RNN (Recurrent Neural Network)
      • Forecasting with LSTM (Long Short Term Memory Network)
      • Understanding Financial Time Series data and forecasting with RNN and LSTM
      • Boosting techniques in Time series data
      • Developing intuition to choose the right network.
      • Dealing with large scale Time Series data

      Libraries Used:

      • Keras (with Tensorflow backend)
      • matplotlib
      • pandas
      • statsmodels
      • prophet
      • pyflux
      • tsfresh
    • Liked Amit  Baldwa


      45 Mins

      Machine learning provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

      Technical analysis shows in graphic form investor sentiment, both greed and fear. Technical analysis attempts to use past stock price and volume information to predict future price movements. Technical analysis of various indicators has been a time-tested strategy for seasoned traders and hedge funds, who have used these techniques to effective turn our profits in Securities Industry.

      Some researchers claim that stock prices conform to the theory of random walk, which is that the future path of the price of a stock is not more predictable than random numbers. However, Stock prices do not follow random walks.

      We will evaluate whether stock returns can be predicted based on historical information.

      Coupled with Machine Learning, we further try to decipher the correlation between the various indicators and identify the set of indicators which appropriately predict the value