Knowledge base

If you're stuck in the classic "no experience, no job; no job, no experience" loop, then Kaggle might just be your secret weapon. I've been there, and here's how you can use Kaggle competitions to get hands-on skills, build killer projects, and impress hiring managers—even if you're just starting out.

Breaking into the world of data science can feel daunting, especially when you’re competing with countless other candidates who tick the standard boxes of technical skills. While meeting the basic expectations is essential, having that extra “spark”—a unique trait that distinguishes you from the crowd—can make all the difference. In today’s tough market, particularly for entry-level positions, leveraging industry-specific knowledge through side projects can be your secret weapon.

The world of machine learning (ML) is booming, and many software engineers (SWEs) are eager to make the leap. Fortunately, your background in coding and industry experience gives you a significant head start. In this post, we'll outline a clear pathway for transitioning into ML by focusing on three key pillars: **Coding**, **Industry Experience**, and **Core ML Knowledge**.

Discover the essential resources to kickstart your journey in Machine Learning and Artificial Intelligence. This blog post curates foundational research papers, influential textbooks, and top-tier courses, all categorized by data modality and key domains to give you access to the seminal works and educational pathways that have shaped the ML/AI landscape.

In this article we explore why the median Machine Learning salary is 15-40% higher than Data Science Salary accross all seniority levels.

In this article, I explore the pragmatic approach to getting into the field of Large Language Models from the perspective of an ML practitioner with most of my prior experience in tabular data

This article compares base salaries for open job positions as of 2023-07-08 in the United States for the main CPU and GPU manufacturers: Nvidia, Intel and AMD.

One of the ongoing debates in any field, including data science, revolves around the actual number of hours spent working per week

Agile, a term inherited from the realm of software development, has enjoyed wide acceptance across different industries and disciplines, including the complex world of data science

In the past few years, the trend of working remotely has been growing in popularity

The key determinant for a lucrative data science salary is primarily its geographic source, with the United States often offering the most competitive rates

This article discusses the data science skills necessary at every stage of the lifecycle of production-level data science and machine learning systems