- Make Pandas 3 Times Faster with PyPolars, by Satyam Kumar - May 31, 2021.
Learn how to speed up your Pandas workflow using the PyPolars library.
- Top 4 Data Extraction Tools, by Zoltan Bettenbuk - May 31, 2021.
Data extraction tools give you the boost you need for gathering information from a multitude of data sources. These four data extraction tools will help liberate you from manual data entry, understand complex documents, and simplify the data extraction process.
- Supercharge Your Machine Learning Experiments with PyCaret and Gradio, by Moez Ali - May 31, 2021.
A step-by-step tutorial to develop and interact with machine learning pipelines rapidly.
- Essential Math for Data Science: Basis and Change of Basis, by Hadrien Jean - May 28, 2021.
In this article, you will learn what the basis of a vector space is, see that any vectors of the space are linear combinations of the basis vectors, and see how to change the basis using change of basis matrices.
- 4 Tips for Dataset Curation for NLP Projects, by Paul Barba - May 28, 2021.
You have heard it before, and you will hear it again. It's all about the data. Curating the right data is also so important than just curating any data. When dealing with text data, many hard-earned lessons have been learned by others over the years, and here are four data curation tips that you should be sure to follow during your next NLP project.
- Great New Resource for Natural Language Processing Research and Applications, by Matthew Mayo - May 27, 2021.
The NLP Index is a brand new resource for NLP code discovery, combining and indexing more than 3,000 paper and code pairs at launch. If you are interested in NLP research and locating the code and papers needed to understand an implement the latest research, you should check it out.
- AI Books you should read in 2021, by Przemek Chojecki - May 27, 2021.
As of late, every year seems to be a "break-out" year for AI. So, it's time for you to get ready for the future in the age of automation. This collection of books will help you prepare for the many opportunities to come, many of which may not have yet been imagined.
- Topic Modeling with Streamlit, by Bryan Patrick Wood - May 26, 2021.
What does it take to create and deploy a topic modeling web application quickly? Read this post to see how the author uses Python NLP packages for topic modeling, Streamlit for the web application framework, and Streamlit Sharing for deployment.
- Top Programming Languages and Their Uses, by Claire D. Costa - May 26, 2021.
The landscape of programming languages is rich and expanding, which can make it tricky to focus on just one or another for your career. We highlight some of the most popular languages that are modern, widely used, and come with loads of packages or libraries that will help you be more productive and efficient in your work.
- Essential Machine Learning Algorithms: A Beginner’s Guide, by Ria Katiyar - May 26, 2021.
Machine Learning as a technology, ensures that our current gadgets and their software get smarter by the day. Here are the algorithms that you ought to know about to understand Machine Learning’s varied and extensive functionalities and their affectivity.
- Write and train your own custom machine learning models using PyCaret, by Moez Ali - May 25, 2021.
A step-by-step, beginner-friendly tutorial on how to write and train custom machine learning models in PyCaret.
- How to Deal with Categorical Data for Machine Learning, by Shelvi Garg - May 24, 2021.
Check out this guide to implementing different types of encoding for categorical data, including a cheat sheet on when to use what type.
- A Guide On How To Become A Data Scientist (Step By Step Approach), by Aditya Agarwal - May 24, 2021.
Becoming a Data Scientists is an exciting path, but you cannot learn data science within one year or six months—instead, it’s a lifetime process that you have to follow with proper dedication and hard work. To guide your journey, the skills outlined here are the first you must acquire to become a data scientist.
- Data Validation in Machine Learning is Imperative, Not Optional, by Aggarwal & Bose - May 24, 2021.
Before we reach model training in the pipeline, there are various components like data ingestion, data versioning, data validation, and data pre-processing that need to be executed. In this article, we will discuss data validation, why it is important, its challenges, and more.
- How to pitch to VCs, explained: The Deck We Used to Raise Capital For Our Open-Source ELT Platform, by John Lafleur - May 21, 2021.
Winning seed funding from venture capitalists is a daunting task, and the pitch is key. Learn how one effective slide deck resulted in a successful early funding round for an open-source start-up, Airbyte.
- Building RESTful APIs using Flask, by Mahadev Easwar - May 21, 2021.
Learn about using the lightweight web framework in Python from this article.
- DataOps: 5 things that you need to know, by Sigmoid - May 20, 2021.
DataOps (Data Operations) has assumed a critical role in the age of big data to drive definitive impact on business outcomes. This process-oriented and agile methodology synergizes the components of DevOps and the capabilities of data engineers and data scientists to support data-focused workloads in enterprises. Here is a detailed look at DataOps.
- Awesome list of datasets in 100+ categories, by Etienne D. Noumen - May 20, 2021.
With an estimated 44 zettabytes of data in existence in our digital world today and approximately 2.5 quintillion bytes of new data generated daily, there is a lot of data out there you could tap into for your data science projects. It's pretty hard to curate through such a massive universe of data, but this collection is a great start. Here, you can find data from cancer genomes to UFO reports, as well as years of air quality data to 200,000 jokes. Dive into this ocean of data to explore as you learn how to apply data science techniques or leverage your expertise to discover something new.
- How to Determine if Your Machine Learning Model is Overtrained, by Charles Martin - May 20, 2021.
WeightWatcher is based on theoretical research (done injoint with UC Berkeley) into Why Deep Learning Works, based on our Theory of Heavy Tailed Self-Regularization (HT-SR). It uses ideas from Random Matrix Theory (RMT), Statistical Mechanics, and Strongly Correlated Systems.
- Differentiable Programming from Scratch, by Guillaume Saupin - May 19, 2021.
In this article, we are going to explain what Differentiable Programming is by developing from scratch all the tools needed for this exciting new kind of programming.
- Animated Bar Chart Races in Python, by Shelvi Garg - May 18, 2021.
A quick and step-by-step beginners project to create an animation bar graph for an amazing Covid dataset.
- The Most In Demand Skills for Data Engineers in 2021, by Terence Shin - May 18, 2021.
If you are preparing to make a career in data or are looking for opportunities to skill-up in your current data-centric role, then this analysis of in-demand skills for 2021, based on over 17,000 Data Engineer job postings, should offer you a good idea as to which programming languages and software tools are increasing and decreasing in importance.
- Easy MLOps with PyCaret + MLflow, by Moez Ali - May 18, 2021.
A beginner-friendly, step-by-step tutorial on integrating MLOps in your Machine Learning experiments using PyCaret.
- Machine Translation in a Nutshell, by Kevin Gray and Dr. Anna Farzin - May 17, 2021.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California for a snapshot of machine translation. Dr. Farzindar also provided the original art for this article.
- Vaex: Pandas but 1000x faster, by Ahmad Anis - May 17, 2021.
If you are working with big data, especially on your local machine, then learning the basics of Vaex, a Python library that enables the fast processing of large datasets, will provide you with a productive alternative to Pandas.
- Binary Classification with Automated Machine Learning, by Derrick Mwiti - May 17, 2021.
Check out how to use the open-source MLJAR auto-ML to build accurate models faster.
- Best Python Books for Beginners and Advanced Programmers, by Claire D. Costa - May 14, 2021.
Let's take a look at nine of the best Python books for both beginners and advanced programmers, covering topics such as data science, machine learning, deep learning, NLP, and more.
- The next-generation of AutoML frameworks, by Aleksandra Plonska and Piotr Plonski - May 14, 2021.
AutoML frameworks are getting better every day, and can provide high-performing ML pipelines, unique data insights, and ML explanations. No longer black-boxes, these powerful tools offer self-documenting capabilities and native Python notebook support.
- DeepMind Wants to Reimagine One of the Most Important Algorithms in Machine Learning, by Jesus Rodriguez - May 14, 2021.
In one of the most important papers this year, DeepMind proposed a multi-agent structure to redefine PCA.
- The NoSQL Know-It-All Compendium, by Alex Williams - May 13, 2021.
Are you a NoSQL beginner, but want to become a NoSQL Know-It-All? Well, this is the place for you. Get up to speed on NoSQL technologies from a beginner's point of view, with this collection of related progressive posts on the subject. NoSQL? No problem!
- 6 side hustles for an aspiring data scientist, by Ahmad Bin Shafiq - May 13, 2021.
As an aspiring data scientist or an employed professional, many opportunities exist for you to offer your skills to a broader audience through side gigs. While the difficulty and risk vary, experiences from applying your data science practice to areas outside your immediate career path can increase your expertise while even increasing your bank account.
- The Explainable Boosting Machine, by Dr. Robert Kübler - May 13, 2021.
As accurate as gradient boosting, as interpretable as linear regression.
- Super Charge Python with Pandas on GPUs Using Saturn Cloud, by Tyler Folkman - May 12, 2021.
Saturn Cloud is a tool that allows you to have 10 hours of free GPU computing and 3 hours of Dask Cluster computing a month for free. In this tutorial, you will learn how to use these free resources to process data using Pandas on a GPU. The experiments show that Pandas is over 1,000,000% slower on a CPU as compared to running Pandas on a Dask cluster of GPUs.
- Machine Learning Pipeline Optimization with TPOT, by Matthew Mayo - May 12, 2021.
Let's revisit the automated machine learning project TPOT, and get back up to speed on using open source AutoML tools on our way to building a fully-automated prediction pipeline.
- Confidence Intervals for XGBoost, by Guillaume Saupin - May 11, 2021.
Read this article about building a regularized Quantile Regression objective.
- Must-have Chrome Extensions For Machine Learning Engineers And Data Scientists, by Himanshu Ragtah - May 11, 2021.
Browser extensions are a productivity secret weapon for hackers and developers. Many machine learning practitioners use Chrome, and this list features must-have Chrome extensions for machine learning engineers and data scientists that you should check out today.
- Similarity Metrics in NLP, by James Briggs - May 10, 2021.
This post covers the use of euclidean distance, dot product, and cosine similarity as NLP similarity metrics.
- Essential Linear Algebra for Data Science and Machine Learning, by Benjamin Obi Tayo - May 10, 2021.
Linear algebra is foundational in data science and machine learning. Beginners starting out along their learning journey in data science--as well as established practitioners--must develop a strong familiarity with the essential concepts in linear algebra.
- Ensemble Methods Explained in Plain English: Bagging, by Claudia Ng - May 10, 2021.
Understand the intuition behind bagging with examples in Python.
- Applying Python’s Explode Function to Pandas DataFrames, by Michael Mosesov - May 7, 2021.
Read this applied Python method to solve the issue of accessing column by date/ year using the Pandas library and functions lambda(), list(), map() & explode().
- Data Preparation in SQL, with Cheat Sheet!, by Stan Pugsley - May 7, 2021.
If your raw data is in a SQL-based data lake, why spend the time and money to export the data into a new platform for data prep?
- A Comprehensive Guide to Ensemble Learning – Exactly What You Need to Know, by Derrick Mwiti - May 6, 2021.
This article covers ensemble learning methods, and exactly what you need to know in order to understand and implement them.
- What is Neural Search?, by Pradeep Sharma - May 6, 2021.
And how to get started with it with no prior experience in Machine Learning.
- Rebuilding My 7 Python Projects, by Kaustubh Gupta - May 5, 2021.
This is how I rebuilt My Python Projects: Data Science, Web Development & Android Apps.
- Deploy a Dockerized FastAPI App to Google Cloud Platform, by Krueger & Franklin - May 4, 2021.
A short guide to deploying a Dockerized Python app to Google Cloud Platform using Cloud Run and a SQL instance.
- Disentangling AI, Machine Learning, and Deep Learning, by Kevin Vu - May 4, 2021.
The field of Artificial Intelligence is extremely broad and captures a winding history through the evolution of various sub-fields that experienced many ups and downs over the years. Appreciating AI within its historical contexts will enhance your communication with the public, colleagues, and potential hiring managers, as well as guide your thinking as you progress in the application and study of state-of-the-art techniques.
- A simple static visualization can often be the best approach, by Kai Wong - May 4, 2021.
How I overengineered a worse solution by making an interactive visualization.
- How To Generate Meaningful Sentences Using a T5 Transformer, by Vatsal Saglani - May 3, 2021.
Read this article to see how to develop a text generation API using the T5 transformer.
- XGBoost Explained: DIY XGBoost Library in Less Than 200 Lines of Python, by Guillaume Saupin - May 3, 2021.
Understand how XGBoost work with a simple 200 lines codes that implement gradient boosting for decision trees.