Category: Python

Python tutorials

Classification, Intermediate, Libraries, NLP, Programming, Python, PyTorch, Supervised, Text, Unstructured Data Leave a comment

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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Advanced, Algorithm, NLP, Python, Sequence Modeling, Supervised, Technique Leave a comment

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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Classification, Intermediate, Libraries, Machine Learning, NLP, Programming, Project, Python, Spark, Text, Unstructured Data Leave a comment

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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Beginner, Data Exploration, E-Commerce, NLP, Python, Technique, Text Leave a comment

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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Advanced, NLP, Python, Social Media, Technique, Text, Unstructured Data, Unsupervised Leave a comment

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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Advanced, Deep Learning, NLP, Project, Python, Text, Unstructured Data, Unsupervised Leave a comment

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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Python

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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Python

Matplotlib Bar Plot – Tutorial and Examples

Introduction Matplotlib is one of the most widely used data visualization libraries in Python. From simple to complex visualizations, it’s the go-to library for most. In this tutorial, we’ll take a look at how to plot a bar plot in Matplotlib. Bar graphs display numerical quantities on one axis and categorical variables on the other, letting you see how many occurrences there are for the different categories. Bar charts can be used for visualizing a time series, as well as […]

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Python

Matplotlib: Change Scatter Plot Marker Size

Introduction Matplotlib is one of the most widely used data visualization libraries in Python. Much of Matplotlib’s popularity comes from its customization options – you can tweak just about any element from its hierarchy of objects. In this tutorial, we’ll take a look at how to change the marker size in a Matplotlib scatter plot. Import Data We’ll use the World Happiness dataset, and compare the Happiness Score against varying features to see what influences perceived happiness in the world: […]

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Python

How to Check if List is Empty in Python

Introduction Lists are one of the four most commonly used data structures provided by Python. Its functionality, extensibility, and ease of use make it useful for implementing various types of functionalities. Python lists have a few interesting characteristics: Mutability – meaning it can change, which means it allows us to easily add and delete entries from it. This is the main difference between Python lists and tuples Homogeneity – meaning all elements within one list have to be of the […]

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