Python tutorials

A Guide to Building an Intelligent Chatbot for Slack using Dialogflow API

Introduction Breakthroughs in the field of Natural Language Processing (NLP) have seen a sudden rise in recent times. The amount of text data available to us is enormous, and data scientists are coming up with new and innovative solutions to parse through it and analyse patterns. From writing entire novels to decoding ancient texts, we have seen a variety of applications for NLP. One of the most popular applications is a chatbot. Organizations like Zomato, Starbucks, Lyft, and Spotify are leveraging […]

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Get Started with PyTorch – Learn How to Build Quick & Accurate Neural Networks (with 4 Case Studies!)

Introduction PyTorch v TensorFlow – how many times have you seen this polarizing question pop up on social media? The rise of deep learning in recent times has been fuelled by the popularity of these frameworks. There are staunch supporters of both, but a clear winner has started to emerge in the last year. PyTorch was one of the most popular frameworks in 2018. It quickly became the preferred go-to deep learning framework among researchers in both academia and the […]

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Predicting Movie Genres using NLP – An Awesome Introduction to Multi-Label Classification

Introduction I was intrigued going through this amazing article on building a multi-label image classification model last week. The data scientist in me started exploring possibilities of transforming this idea into a Natural Language Processing (NLP) problem. That article showcases computer vision techniques to predict a movie’s genre. So I had to find a way to convert that problem statement into text-based data. Now, most NLP tutorials look at solving single-label classification challenges (when there’s only one label per observation). […]

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10 Powerful Applications of Linear Algebra in Data Science (with Multiple Resources)

Overview Linear algebra powers various and diverse data science algorithms and applications Here, we present 10 such applications where linear algebra will help you become a better data scientist We have categorized these applications into various fields – Basic Machine Learning, Dimensionality Reduction, Natural Language Processing, and Computer Vision   Introduction If Data Science was Batman, Linear Algebra would be Robin. This faithful sidekick is often ignored. But in reality, it powers major areas of Data Science including the hot […]

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Build Your First Text Classification model using PyTorch

Overview Learn how to perform text classification using PyTorch Grasp the importance of Pack Padding feature Understand the key points involved while solving text classification Introduction I always turn to State of the Art architectures to make my first submission in data science hackathons. Implementing the State of the Art architectures has become quite easy thanks to deep learning frameworks such as PyTorch, Keras, and TensorFlow. These frameworks provide an easy way to implement complex model architectures and algorithms with […]

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Machine Learning in Cyber Security — Malicious Software Installation

Introduction Monitoring of user activities performed by local administrators is always a challenge for SOC analysts and security professionals. Most of the security framework will recommend the implementation of a whitelist mechanism. However, the real world is often not ideal. You will always have different developers or users having local administrator rights to bypass controls specified. Is there a way to monitor the local administrator activities?

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Save Plot as Image with Matplotlib

Introduction Matplotlib is one of the most widely used data visualization libraries in Python. It’s common to share Matplotlib plots and visualizations with others. In this article, we’ll take a look at how to save a plot/graph as an image file using Matplotlib. Creating a Plot Let’s first create a simple plot: import matplotlib.pyplot as plt import numpy as np x = np.arange(0, 10, 0.1) y = np.sin(x) plt.plot(x, y) plt.show() Here, we’ve plotted a sine function, starting at 0 […]

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Python with Pandas: DataFrame Tutorial with Examples

Introduction Pandas is an open-source Python library for data analysis. It is designed for efficient and intuitive handling and processing of structured data. The two main data structures in Pandas are Series and DataFrame. Series are essentially one-dimensional labeled arrays of any type of data, while DataFrames are two-dimensional, with potentially heterogenous data types, labeled arrays of any type of data. Heterogenous means that not all “rows” need to be of equal size. In this article we will go through […]

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Remove Element from an Array in Python

Introduction This tutorial will go through some common ways for removing elements from Python arrays. Here’s a list of all the techniques and methods we’ll cover in this article: Arrays in Python Arrays and lists are not the same thing in Python. Although lists are more commonly used than arrays, the latter still have their use cases. The main difference between the two is that lists can be used to store arbitrary values. They are also heterogeneous which means they […]

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Guide to String Interning in Python

Introduction One of the first things you encounter while learning the basics of programming is the concept of strings. Similar to various programming languages, Python strings are arrays of bytes representing Unicode characters – an array or sequence of characters. Python, unlike many programming languages, doesn’t have a distinct character datatype, and characters are considered strings of length 1. You can define a string using single or double quotation marks, for example, a = “Hello World” or a = ‘Hello […]

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