This detailed guide explores an unsupervised and supervised learning two-stage approach with LDA and BERT to develop a domain-specific document categorizer on unlabeled documents.
The intension for most data science projects is to build something that people use. Creating something purposeful requires a solid infrastructure and processes that keeps problem-solving front-and-center for your audience.
We cover 11 best Data Science Education platforms for 11 different use cases, ranging from specific languages to hands-on learners, to the best free option.
Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.
Personalization drives growth and is a touchstone of good customer experience. Personalization driven through machine learning can enable companies to improve this experience while improving ROI for marketing campaigns. However, challenges exist in these techniques for when personalization makes sense and how and when specific options are recommended.
Access to high-quality, noise-free, large-scale datasets is crucial for training complex deep neural network models for computer vision applications. Many open-source datasets are developed for use in image classification, pose estimation, image captioning, autonomous driving, and object segmentation. These datasets must be paired with the appropriate hardware and benchmarking strategies to optimize performance.
Rendezvous Architecture helps you run and choose outputs from a Champion model and many Challenger models running in parallel without many overheads. The original approach works well for smaller data sets, so how can this idea adapt to big data pipelines?
The notion of Agile in software development has made waves across industries with its revolution for productivity. Can the same benefits be applied to the often arduous task of annotating data sets for machine learning?
Using Ray, you can take Python code that runs sequentially and transform it into a distributed application with minimal code changes. Read on to find out why you should use Ray, and how to get started.
Having access to broad and detailed population data can potentially offer enormous value to any organization looking to interact with specific demographics. However, access alone is not sufficient without being able to leverage advanced techniques to explore and visualize the data.
The second edition of the classic "An Introduction to Statistical Learning, with Applications in R" was published very recently, and is now freely-available via PDF on the book's website.
SQL is a very important skill for data analysts and data scientists. However, when you are just starting out learning in the field, how can you practice querying with SQL if you don’t have any data stored in a database?
This article summarizes the most common mistakes to avoid and outline best practices to follow in programming in general. Follow these tips to speed up the code review iteration process and be a rockstar developer in your reviewer’s eyes!
What is a Modern Data Stack and how do you deploy one? This guide will motivate you to start on this journey with setup instructions for Airbyte, BigQuery, dbt, Metabase, and everything else you need using Terraform.
When Data Scientists use chi square test for feature selection, they just merely go by the ritualistic “If your p-value is low, the null hypothesis must go”. The automated function they use behaves no differently.
After analyzing 900+ data science interview questions from companies over the past few years, the most common data science interview question categories are reviewed in this guide, each explained with an example.
If you are a nerd-ish data scientist who wants to start working as an independent (remote) freelance data scientist, then these four practical tips can help you transition from the traditional 9-to-5 job to a dynamic experience as a remote contractor, just as the author did three years ago.
There is always a lot to learn in machine learning. Whether you are new to the field or a seasoned practitioner and ready for a refresher, understanding these key concepts will keep your skills honed in the right direction.
Typically, development and testing ETL pipelines is done on real environment/clusters which is time consuming to setup & requires maintenance. This article focuses on the development and testing of ETL pipelines locally with the help of Docker & LocalStack. The solution gives flexibility to test in a local environment without setting up any services on the cloud.
Learning Data Science and Machine Learning: First Steps After The Roadmap, by Harshit Tyagi - Aug 24, 2021.
Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.
Learning Data Science and Machine Learning: First Steps After The Roadmap
Prefect: How to Write and Schedule Your First ETL Pipeline with Python
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