Python tutorials

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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Summarize Twitter Live data using Pretrained NLP models

Introduction Twitter users spend an average of 4 minutes on social media Twitter. On an average of 1 minute, they read the same stuff. It shows that users spend around 25% of their time reading the same stuff. Also, most of the tweets will not appear on your dashboard. You may get to know the trending topics, but you miss not trending topics. In trending topics, you might only read the top 5 tweets and their comments. So, what are […]

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Tired of Reading Long Articles? Text Summarization will make your task easier!

This article was published as a part of the Data Science Blogathon. Introduction Millions of web pages and websites exist on the Internet today. Going through a vast amount of content becomes very difficult to extract information on a certain topic. Google will filter the search results and give you the top ten search results, but often you are unable to find the right content that you need. There is a lot of redundant and overlapping data in the articles […]

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Seaborn Distribution/Histogram Plot – Tutorial and Examples

Introduction Seaborn is one of the most widely used data visualization libraries in Python, as an extension to Matplotlib. It offers a simple, intuitive, yet highly customizable API for data visualization. In this tutorial, we’ll take a look at how to plot a histogram plot in Seaborn. We’ll cover how to plot a histogram with Seaborn, how to change Histogram bin sizes, as well as plot Kernel Density Estimation plots on top of Histograms and show distribution data instead of […]

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