Python tutorials

Reading and Writing XML Files in Python with Pandas

Introduction XML (Extensible Markup Language) is a markup language used to store structured data. The Pandas data analysis library provides functions to read/write data for most of the file types. For example, it includes read_csv() and to_csv() for interacting with CSV files. However, Pandas does not include any methods to read and write XML files. In this article, we will take a look at how we can use other modules to read data from an XML file, and load it […]

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Step by step guide to building sentiment analysis model using graphlab

I have been using graph lab for quite some time now. The first Kaggle competition I used it for was Click Trough Rate (CTR) and I was amazed to see the speed at which it can crunch such big data. Over last few months, I have realised much broader applications of GraphLab. In this article I will take up the text mining capability of GraphLab and solve one of the Kaggle problems. I will be referring to this problem with […]

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Natural Language Processing Made Easy – using SpaCy (​in Python)

Introduction Natural Language Processing is one of the principal areas of Artificial Intelligence. NLP plays a critical role in many intelligent applications such as automated chat bots, article summarizers, multi-lingual translation and opinion identification from data. Every industry which exploits NLP to make sense of unstructured text data, not just demands accuracy, but also swiftness in obtaining results. Natural Language Processing is a capacious field, some of the tasks in nlp are – text classification, entity detection, machine translation, question […]

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Building a FAQ Chatbot in Python – The Future of Information Searching

Introduction What do we do when we need any information? Simple: “We Ask, and Google Tells”. But if the answer depends on multiple variables, then the existing Ask-Tell model tends to sputter. State of the art search engines usually cannot handle such requests. We would have to search for information available in bits and pieces and then try to filter and assemble relevant parts together. Sounds time consuming, doesn’t it? Source: Inbenta This Ask-Tell model is evolving rapidly with the […]

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A Comprehensive Guide to Understand and Implement Text Classification in Python

Improving Text Classification Models While the above framework can be applied to a number of text classification problems, but to achieve a good accuracy some improvements can be done in the overall framework. For example, following are some tips to improve the performance of text classification models and this framework. 1. Text Cleaning : text cleaning can help to reducue the noise present in text data in the form of stopwords, punctuations marks, suffix variations etc. This article can help to understand how […]

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Top 5 Machine Learning GitHub Repositories & Reddit Discussions (October 2018)

Introduction “Should I use GitHub for my projects?” – I’m often asked this question by aspiring data scientists. There’s only one answer to this – “Absolutely!”. GitHub is an invaluable platform for data scientists looking to stand out from the crowd. It’s an online resume for displaying your code to recruiters and other fellow professionals. The fact that GitHub hosts open-source projects from the top tech behemoths like Google, Facebook, IBM, NVIDIA, etc. is what adds to the gloss of […]

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Introduction to Flair for NLP: A Simple yet Powerful State-of-the-Art NLP Library

Introduction Last couple of years have been incredible for Natural Language Processing (NLP) as a domain! We have seen multiple breakthroughs – ULMFiT, ELMo, Facebook’s PyText, Google’s BERT, among many others. These have rapidly accelerated the state-of-the-art research in NLP (and language modeling, in particular). We can now predict the next sentence, given a sequence of preceding words. What’s even more important is that machines are now beginning to understand the key element that had eluded them for long. Context! Understanding context […]

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How do Transformers Work in NLP? A Guide to the Latest State-of-the-Art Models

Overview The Transformer model in NLP has truly changed the way we work with text data Transformer is behind the recent NLP developments, including Google’s BERT Learn how the Transformer idea works, how it’s related to language modeling, sequence-to-sequence modeling, and how it enables Google’s BERT model   Introduction I love being a data scientist working in Natural Language Processing (NLP) right now. The breakthroughs and developments are occurring at an unprecedented pace. From the super-efficient ULMFiT framework to Google’s […]

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How to use a Machine Learning Model to Make Predictions on Streaming Data using PySpark

Overview Streaming data is a thriving concept in the machine learning space Learn how to use a machine learning model (such as logistic regression) to make predictions on streaming data using PySpark We’ll cover the basics of Streaming Data and Spark Streaming, and then dive into the implementation part   Introduction Picture this – every second, more than 8,500 Tweets are sent, more than 900 photos are uploaded on Instagram, more than 4,200 Skype calls are made, more than 78,000 […]

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A Beginner’s Guide to Exploratory Data Analysis (EDA) on Text Data (Amazon Case Study)

The Importance of Exploratory Data Analysis (EDA) There are no shortcuts in a machine learning project lifecycle. We can’t simply skip to the model building stage after gathering the data. We need to plan our approach in a structured manner and the exploratory data analytics (EDA) stage plays a huge part in that. I can say this with the benefit of hindsight having personally gone through this situation plenty of times. In my early days in this field, I couldn’t […]

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