Leveraging this pandas GUI to speed up your data exploration process — Data wrangling and exploratory data analysis are some of the most important, yet underrated tasks in the data science process. Most data scientists are familiar with Pandas — a Python library that has become the Swiss army knife for data manipulation and analysis. While Pandas has many helpful functions, performing…

Analyzing the ethical implications and limitations of using artificial intelligence to replace human jobs — In 2005, Ray Kurzweil, a famous futurist, described the Singularity — a point in time when machine intelligence becomes far more powerful than all human intelligence combined. One idea that is closely tied to the Singularity is the automation of human jobs with artificial intelligence. The automation of human labor…

How you can use this tree-based algorithm to detect outliers in your data — Anomaly detection is a frequently overlooked area of machine learning. It doesn’t seem anywhere near as flashy as deep learning or natural language processing and often gets skipped entirely in machine learning courses. However, anomaly detection is still important and has applications ranging from data preprocessing to fraud detection and…

An introduction to a more sophisticated approach to topic modeling. — Topic modeling is a problem in natural language processing that has many real-world applications. Being able to discover topics within large sections of text helps us understand text data in greater detail. For many years, Latent Dirichlet Allocation (LDA) has been the most commonly used algorithm for topic modeling. The…

An introduction to this modern gradient-boosting library — If you’ve worked as a data scientist, competed in Kaggle competitions, or even browsed data science articles on the internet, there’s a high chance that you’ve heard of XGBoost. Even today, it is often the go-to algorithm for many Kagglers and data scientists working on general machine learning tasks. While…

Using this Python library to send model training updates. — Imagine this scenario — you’re working on a deep learning project and just started a time-consuming training job on a GPU. Based on your estimates, it will take you about fifteen hours for your job to finish. Obviously, you don’t want to watch your model train for that long. …

How you can use this powerful API for analyzing articles on the web. — In the Information Age, we have a huge amount of information available to us at our fingertips. The internet is so large that actually estimating its size is a complex task. …

How to deploy your ML models quickly with this API-building tool. — Knowing how to integrate machine learning models into usable applications is an important skill for data scientists. In my previous article linked below, I demonstrated how you can quickly and easily build web apps to showcase your models with Streamlit. How you can quickly build ML web apps with Streamlit. The quickest way to embed your models into web apps.towardsdatascience.com

The quickest way to embed your models into web apps. — If you’re a data scientist or a machine learning engineer, you are probably reasonably confident in your ability to build models to solve real-world business problems. But how good are you at front-end web development? Can you build a visually appealing web application to showcase your models? …