# Beginner’s Guide To Text Classification Using PyCaret

Introduction Have you ever solved a Machine Learning problem in just one go? Solving a problem using machine learning isn’t straightforward. It involves various steps to come up with an accurate solution. The process/steps to be followed for solving an ml problem is known as ML Pipeline/ML Cycle. ML Pipeline/ ML Cycle (Credits: https://medium.com/analytics-vidhya/machine-learning-development-life-cycle-dfe88c44222e) As shown in the figure, the Machine Learning pipeline consists of different steps like: Understand Problem Statement, Hypothesis Generation, Exploratory Data Analysis, Data Preprocessing, Feature Engineering, […]

# A friendly guide to NLP: Bag-of-Words with Python example

1. A Quick Example Let’s look at an easy example to understand the concepts previously explained. We could be interested in analyzing the reviews about Game of Thrones: Review 1: Game of Thrones is an amazing tv series! Review 2: Game of Thrones is the best tv series! Review 3: Game of Thrones is so great In the table, I show all the calculations to obtain the Bag-Of-Words approach: Each row corresponds to a different review, while the rows are […]

# Predict the next word of your text using Long Short Term Memory (LSTM)

This article was published as a part of the Data Science Blogathon Introduction: https://sm.mashable.com/t/mashable_in/photo/default/shutterstock-1208129407_trm5.960.jpg Natural language processing has been an area of research and used widely in different applications. We often love texting each other and find that whenever we try to type a text a suggestion poops up trying to predict the next word we want to write. This process of prediction is one of the applications NLP deals with. We have made huge progress here and we can use […]

# Getting started with NLP using NLTK Library

1010010   01101001   01110100   01101000   01101001  01101011   01100001 Did you understand the above binary code? If yes, then you’re a computer. If no, then you’re a Human. 🙂 I know it’s a difficult task for us to understand binary code just like computers because binary code is a Machine Understandable Language. Likewise, even computers don’t understand human language. So, how to make computers understand human language? The answer is Natural Language Processing. With the help of NLP, we can teach computers […]

# Text Generation Using Bidirectional LSTM – A Walk-through in Tensorflow

This article was published as a part of the Data Science Blogathon Text Generation The Text Generation is a Natural Language Processing task that involves automatically generating meaningful texts. We can also utilize the Text Generation process for Autocomplete. Initially, we provide a prompt, which is a text that is used as the base to generate texts. The model will generate texts based on the prompt, the predicted text will be added to the base prompt and it is fed again […]

# Build an end-end Currency Convertor chatbot with Python and Dialogflow

This article was published as a part of the Data Science Blogathon Introduction Hello all, Hope you are fine. In this tutorial we will learn how to create chatbots using Dialogflow and python, as well we will learn the deployment of chatbots to telegram. In our previous articles, we have learned to create a simple rule-based chatbot using simple python and NLTK libraries. I would like to request you to have a look at the article creating a simple chatbot […]

# New Anaphora and Co-reference Resolution Technique for Biographies

This article was published as a part of the Data Science Blogathon Introduction Biographies of many famous personalities are very insightful and inspiring. Although, one may not want to read the whole document. In order to just get the important points from the biography, one can generate a summary of the biography. The summary is generated by giving weights to all the words. Sometimes, anaphoras can be predicted by the machine as a separate word which in return produces a less […]

# Email Spam Detection – A Comparative Analysis of 4 Machine Learning Models

This article was published as a part of the Data Science Blogathon Introduction This article aims to compare four different deep learning and machine learning algorithms to build a spam detector and evaluate their performances. The dataset we used was from a shuffled sample of email subjects and bodies containing both spam and ham emails in numerous proportions, which we converted into lemmas. Email Spam Detection is one of the most effective projects of Deep learning but this is often also […]