Insights from Data

Data is everywhere. Change your view to get insight.

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Top 3 Pandas Functions You Don't Know About (Probably)

Functions that saved me a lot of time and frustration

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A Beginner-Friendly Guide to PyTorch and How it Works from Scratch

Every once in a while, there comes a library or framework that reshapes and reimagines how we look at the field of deep learning. The…

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Dunder Data Challenge #3 — Optimal Solution

The ‘optimal’ solution to Dunder Data Challenge #3 will be presented. It does not use apply and shows a 20x speedup over the naive…

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30 Helpful Python Snippets That You Can Learn in 30 Seconds or Less

Short Python snippets that you can quickly learn and use in your work or personal needs

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How to Deploy a Machine Learning Model

Creating a production-ready API with FastAPI + Uvicorn

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What’s New In Pandas Version 0.25.0?

An Overview Of Great Features In Pandas 0.25 Python Library

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Python Libraries for Interpretable Machine Learning

4 libraries for better visualisation, explanation and interpretation of models

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Normalization vs Standardization — Quantitative analysis

Stop using StandardScaler from Sklearn as a default feature scaling method can get you a boost of 7% in accuracy!

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Imbalanced Class Sizes and Classification Models: A Cautionary Tale

For a recent data science project, I developed a supervised learning model to classify the booking location of a first-time user of the…

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“Let’s Talk About Machine Learning Ensemble Learning In Python” by Farhad Malik https://link.medium.com/E9AIikKjrY

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Ultimate Guide to Understand and Implement Natural Language Processing (with codes in Python)

Overview Complete guide on natural language processing (NLP) in Python Learn various techniques for implementing NLP including parsing & text processing Understand how to use NLP for text feature engineering Introduction According to industry estimates, only 21% of the available data is present in structured form. Data is being generated

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Basics of BASH for Beginners.

Learn about some of the most useful BASH commands and the utility they offer.

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The NumPy package is the workhorse of data analysis, machine learning, and scientific computing in the python ecosystem. It vastly simplifies manipulating and crunching vectors and matrices. Some of python’s leading package rely on NumPy as a fundamental piece of their infrastructure (examples include scikit-learn, SciPy, pandas, and tensorflow). Beyond the ability to slice and dice numeric data, mastering numpy will give you an edge when dealing and debugging with advanced usecases in these libraries. In this post, we’ll look at some of the main ways to use NumPy and how it can represent different types of data (tables, images, text…etc) before we an serve them to machine learning models.

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Python Environment Management with Conda (Python 2 + 3, Using Multiple Versions of Python)

Coming across an ImportError similar to the one in the image below can be annoying.

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The Data Science Interview Study Guide

121 resources to help you land your data science dream job

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28 Jupyter Notebook Tips, Tricks, and Shortcuts for Data Science

Jupyter Notebook is a powerful tool for data analysis. Here are 28 tips, tricks, and shortcuts to turn you into a Jupyter notebooks power user!

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Become a Pro at Pandas, Python’s data manipulation Library

The pandas library is the most popular data manipulation library for python. It provides an easy way to manipulate data through its…