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Now into the monthly picks! We pick these articles based on readership, fans, and views a specific piece gets. We hope you enjoy reading them as much as we did. Also, we started doing something new! We will pick our top ten performing articles, and our editors will choose one to two essays that didn’t have outstanding performance, but due to its quality — made the cut for the month.
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📚 Editor’s choice featured articles for the month ↓ 📚
The first step in any data science project is EDA. This article will explain why each step in the EDA is important and why we should care about what we learn from our data. EDA or Exploratory Data Analysis is the process of understanding what data we have in our dataset before we start finding solutions to our problem. In other words — it is the act of analyzing the data without biased assumptions in order to effectively preprocess the dataset for modeling…
Monte Carlo Simulation An In-depth Tutorial with Python by Pratik Shukla, Roberto Iriondo
A Monte Carlo method is a technique that uses random numbers and probability to solve complex problems. The Monte Carlo simulation, or probability simulation, is a technique used to understand the impact of risk and uncertainty in financial sectors, project management, costs, and other forecasting machine learning models…
Don’t get confused with these two similar but different patterns, and know which one to use when. This difference is important to not just generic Software Engineers, but also to Data Engineers and is the basis for the understanding event-driven architectures for data pipelines. Let’s look at both of them individually before we eventually list-out the differences…
A machine learning algorithm (such as classification, clustering, or regression) uses a training dataset to determine weight factors that can be applied to unseen data for predictive purposes. Behind every machine learning model is an optimization algorithm that relies heavily on calculus. In this article, we discuss one such optimization algorithm, namely, the Gradient Descent Approximation (GDA), and we’ll show how it can be used to build a simple regression estimator…
In this to guide, I am going to show you some of the most common pitfalls that can cause otherwise perfectly good Pandas code to be too slow for any time-sensitive applications, and walk through a set of tips and tricks to avoid them.
Let’s remind ourselves what is pandas, apart from a cute animal 🐼. Its a widely used library for data analysis and manipulation that load all the data into RAM.
Data Validation Framework in Apache Spark for Big Data Migration Workloads by Karthikeyan Siva Baskaran
Quality Assurance Testing is one of the key areas in Bigdata. Data quality issues may ruin the success of many Big Data, data lake, ETL projects. Whether the data is big or small, the need for data quality doesn’t change. High-quality data is the absolute driver to get insights from it. The quality of data is measured based on whether it satisfies the business by deriving the necessary insights…
The Neural Network has been developed to mimic a human brain. Though we are not there yet, neural networks are very efficient in machine learning. It was popular in the 1980s and 1990s. Recently it has become more popular. Computers are fast enough to run a large neural network in a reasonable time. In this article, I will discuss how to implement a neural network.
Still waiting for your Hogwarts letter?
Want to enjoy the feast in the Great Hall?
Explore the secret passages in Hogwarts?
Buy your first wand from Ollivander’s?
*sigh* You are not alone.
I have (after all this time?) always been obsessed with Harry Potter, and I recently started learning neural networks. It’s fascinating to see how creative you can get with Deep Learning, so I thought, why not brew them up?
This article runs you through the entire code I wrote to implement it.
But for all the Hermione’s out there, you can directly find the GitHub code here and run it yourself!
The objective of this post is to build some more understanding of Apache Kafka concepts such as Topics, Partitions, Consumer, and Consumer Groups. Kafka’s basic concepts have been covered in my previous article. As we know, messages in Kafka are categorized or stored inside Topics. In simple terms, Topic can be construed as a Database table. Kafka Topics inside is broken down into partitions…
Natural language is a language that is used for everyday communication between humans. It is highly unstructured in nature for both text and speech, thus making it difficult to parse and comprehend by machines. Natural language processing (“NLP”) is concerned with the interaction between natural human language and computers. It is an intersection between the fields of linguistics, computer science, and artificial intelligence.
Streamlining an inference pipeline on the latest fine-tuned NLP model is a must for fast prototyping. However, with the plethora of diverse model architectures and NLP libraries to choose from, it can make prototyping a time-consuming task. As such, we’ve created The NLP Model Forge. A database/code generator for 1,400 fine-tuned models that were carefully curated from top NLP research companies such as Hugging Face, Facebook (ParlAI), DeepPavlov, and AI2.
I have recently started writing about Data Science and Python on Medium. One of the time taking activity when writing any technical articles is to provide the reproducible supporting code. I have been using GitHub Gist and sometimes a screenshot of the code from Jupyter Notebook or other Python editor. But it all takes time as we need to toggle between Medium editor and Gist or other Python editor…
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Exploratory Data Analysis (EDA) — Don’t ask how, ask what… and More! was originally published in Towards AI — Multidisciplinary Science Journal on Medium, where people are continuing the conversation by highlighting and responding to this story.
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