Python has become a de facto lingua franca for machine learning. It is not a difficult language to learn, but if you are not particularly familiar with the language, there are some tips that can help you learn faster or better. In this post, you will discover…

This tutorial is designed for anyone looking for an understanding of how recurrent neural networks (RNN) work and how to use them via the Keras deep learning library. While the Keras library provides all the methods required for solving problems and building…

Posted by Ali Kemal Sinop, Senior Research Scientist, Google Research and Mahmuda Ahmed, Senior Software Engineer, Google Maps Design tec...

Posted by Jae Hun Ro, Software Engineer and Ananda Theertha Suresh, Research Scientist, Google Research Federated learning is a machine ...

Posted by Maarten Bosma, Research Engineer and Jason Wei, AI Resident, Google Research For a machine learning model to generate meaningfu...

Posted by Lusann Yang, Software Engineer, Google Research, Applied Science Team A crystalline material has atoms systematically arranged...

Posted by Cat Armato, Program Manager, Google Research The International Conference on Computer Vision 2021 (ICCV 2021), one of the worl...

In this blog post I will show you how to build a simple, but useful and good-looking dashboard to present your data - in three simple steps!

Also: How to Ace Data Science Interview by Working on Portfolio Projects; AutoML: An Introduction Using Auto-Sklearn and Auto-PyTorch; How to Build Strong Data Science Portfolio as a Beginner; 8 Must-Have Git Commands for Data Scientists

While the field of data science continues to evolve with exciting new progress in analytical approaches and machine learning, there remain a core set of skills that are foundational for all general practitioners and specialists, especially those who want…

While there may be plenty of room for advancement even when busy, how to achieve that isn’t always clear. In that spirit, here are five ways you can impress your company leadership.

Principal component analysis (PCA) is an unsupervised machine learning technique. Perhaps the most popular use of principal component analysis is dimensionality reduction. Besides using PCA as a data preparation technique, we can also use it to help visualize…

Over the past few years, the data engineering market has seen tremendous growth. The acceleration of the data engineering market prompted us to create a new report specifically for data engineering professionals. You can download both the 2021 Data Engineering…

What are the differences between these two popular tech roles?

The 20 Python Packages You Need For Machine Learning and Data Science; How to Ace Data Science Interview by Working on Portfolio Projects; Deploying Your First Machine Learning API; Real Time Image Segmentation Using 5 Lines of Code; What is Clustering and…

Choosing what to include in your data science portfolio during the job search is the most important part of the process. Each project should be well-structured so that a hiring manager can assess your skills quickly. To help you get started, we highlight…

Leverage the powerful data wrangling tools in R’s dplyr to clean and prepare your data.