Machine Learning

Auto-Sklearn for Automated Machine Learning in Python

Last Updated on September 12, 2020 Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. Auto-Sklearn is an open-source library for performing AutoML in Python. It makes use of the popular Scikit-Learn machine learning library for data transforms and machine learning algorithms and uses a Bayesian Optimization search procedure to efficiently discover a top-performing model pipeline for a given dataset. In this tutorial, you will discover how to […]

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Beginner, Data Science, Libraries, NLP, Programming, Python, Text, Unstructured Data Leave a comment

Beginners Tutorial for Regular Expressions in Python

Importance of Regular Expressions In last few years, there has been a dramatic shift in usage of general purpose programming languages for data science and machine learning. This was not always the case – a decade back this thought would have met a lot of skeptic eyes! This means that more people / organizations are using tools like Python / JavaScript for solving their data needs. This is where Regular Expressions become super useful. Regular expressions are normally the default way […]

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Classification, Data Science, Intermediate, NLP, Project, Python, Supervised, Technique, Text, Unstructured Data Leave a comment

Sentiment Analysis of Twitter Posts on Chennai Floods using Python

Introduction The best way to learn data science is to do data science. No second thought about it! One of the ways, I do this is continuously look for interesting work done by other community members. Once I understand the project, I do / improve the project on my own. Honestly, I can’t think of a better way to learn data science. As part of my search, I came across a study on sentiment analysis of Chennai Floods on Analytics Vidhya. […]

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Beginner, Libraries, NLP, Python, Text, Unstructured Data Leave a comment

How to Use Texthero to Prepare a Text-based Dataset for Your NLP Project

Introduction Natural Language Processing (NLP) is one of the most important fields of study and research in today’s world. It has many applications in the business sector such as chatbots, sentiment analysis, and document classification. Preprocessing and representing text is one of the trickiest and most annoying parts of working on an NLP project. Text-based datasets can be incredibly thorny and difficult to preprocess. But fortunately, the latest Python package called Texthero can help you solve these challenges. What is […]

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Advanced, NLP, Project, Python, Ranking, Technique, Text, Unstructured Data, Word Embeddings Leave a comment

Information Retrieval using word2vec based Vector Space Model

Overview Learn about Information Retrieval (IR), Vector Space Models (VSM), and Mean Average Precision (MAP) Create a project on Information Retrieval using word2vec based Vector Space Model   Introduction “Google it!”- Isn’t it something we say every day? Whenever we come across something that we don’t know about, we “Google it.” Google Search is a great tool that can be used for even finding a needle from a haystack. This generation absolutely relies on Google for answers to all kinds […]

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Algorithm, Deep Learning, Intermediate, NLP, Supervised, Text, Unstructured Data Leave a comment

A Simple Introduction to Sequence to Sequence Models

Overview In this article, I would give you an overview of sequence to sequence models which became quite popular for different tasks like machine translation, video captioning, image captioning, question answering, etc. Prerequisites: The reader should already be familiar with neural networks and, in particular, recurrent neural networks (RNNs). In addition, knowledge of LSTM or GRU models is preferable. If you are not familiar with LSTM I would prefer you to read LSTM- Long Short-Term Memory.

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NLP

How to develop LSTM recurrent neural network models for text classification problems in Python using Keras deep learning library

How to develop LSTM recurrent neural network models for text classification problems in Python using Keras deep learning library Automatic text classification or document classification can be done in many different ways in machine learning as we have seen before. This article aims to provide an example of how a Recurrent Neural Network (RNN) using the

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NLP

Deep dive into multi-label classification..! (With detailed Case Study)

We first convert the comments to lower-case and then use custom made functions to remove html-tags, punctuation and non-alphabetic characters from the comments. import nltkfrom nltk.corpus import stopwordsfrom nltk.stem.snowball import SnowballStemmerimport reimport sysimport warningsdata = data_rawif not sys.warnoptions:warnings.simplefilter(“ignore”)def cleanHtml(sentence):cleanr = re.compile(”)cleantext = re.sub(cleanr, ‘ ‘, str(sentence))return cleantextdef cleanPunc(sentence): #function to clean the word of any punctuation or special characterscleaned Visit source site to finish reading.

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NLP

Building a Simple Chatbot from Scratch in Python (using NLTK)

A chatbot is an artificial intelligence-powered piece of software in a device (Siri, Alexa, Google Assistant etc), application, website or other networks that try to gauge consumer’s needs and then assist them to perform a particular task like a commercial transaction, hotel booking, form submission etc . Today almost every company has a chatbot deployed to engage with the users. Some of the ways in which companies are using chatbots are: To deliver flight information to connect customers and their […]

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Machine Learning, NETWORK PRUNING

A Gradient Flow Framework For Analyzing Network Pruning

Recent network pruning methods focus on pruning models early-on in training. To estimate the impact of removing a parameter, these methods use importance measures that were originally designed for pruning trained models… Despite lacking justification for their use early-on in training, models pruned using such measures result in surprisingly minimal accuracy loss. To better explain this behavior, we develop a general, gradient-flow based framework that relates state-of-the-art importance measures through an order of time-derivative of the norm of model parameters. […]

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